Additive MDA
Elen Le Foll
25/03/2020
This script is part of the Online Appendix to my PhD thesis.
Please cite as: Le Foll, Elen. 2022. Textbook English: A Corpus-Based Analysis of the Language of EFL textbooks used in Secondary Schools in France, Germany and Spain. PhD thesis. Osnabrück University.
For more information, see: https://elenlefoll.github.io/TextbookEnglish/
Please note that the plot dimensions in this notebook have been optimised for the print version of the thesis.
Built with R 4.0.3
# Colours used in Register Studies paper and included in Open Access plots published on Zenodo:
colours <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
scales::show_col(colours)
colours <- colours[6:1]
# Colour scheme used in PhD thesis:
colours = c("#F9B921", "#A18A33", "#722672", "#BD241E", "#15274D", "#D54E1E")
scales::show_col(colours)
Import data
Textbook data for additive MDAs using Biber’s 1988 model of general English as a baseline
# Textbook Corpus
TxBcounts <- read.delim(here("data", "TxB900MDA_counts.tsv"), header = TRUE)
glimpse(TxBcounts)## Rows: 1,950
## Columns: 101
## $ Filename <chr> "._Access_1_Informative_0001.txt", "Access_1_Informative_000…
## $ Level <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", …
## $ Tokens <int> 89, 664, 718, 731, 768, 768, 773, 775, 765, 779, 827, 1042, …
## $ AWL <dbl> 1.5169, 4.2560, 4.0543, 4.2585, 3.9375, 3.9779, 4.0763, 3.90…
## $ TTR <dbl> 0.191011, 0.535000, 0.362500, 0.397500, 0.387500, 0.360000, …
## $ X. <dbl> 0.0000, 0.0000, 0.1393, 0.0000, 0.0000, 0.0000, 0.0000, 0.12…
## $ X..1 <dbl> 0.0000, 0.3012, 0.5571, 0.5472, 0.1302, 0.0000, 0.0000, 0.12…
## $ X.. <dbl> 0.0000, 0.4518, 0.1393, 0.1368, 0.2604, 0.2604, 0.0000, 0.25…
## $ X..2 <dbl> 0.0000, 5.2711, 2.5070, 1.5048, 0.5208, 1.5625, 1.5524, 1.03…
## $ X.LRB. <dbl> 0.0000, 0.3012, 0.9749, 0.9576, 1.1719, 0.1302, 0.7762, 1.80…
## $ X.RRB. <dbl> 0.0000, 0.3012, 4.0390, 6.7031, 5.9896, 4.6875, 5.1746, 5.16…
## $ . <dbl> 0.0000, 8.1325, 13.3705, 13.4063, 13.0208, 12.1094, 13.0660,…
## $ X..3 <dbl> 0.0000, 1.8072, 4.4568, 3.1464, 2.7344, 1.5625, 3.6223, 2.83…
## $ AMP <dbl> 0.0000, 0.9036, 0.0000, 0.0000, 0.0000, 0.0000, 0.1294, 0.00…
## $ ANDC <dbl> 0.0000, 0.1506, 0.2786, 0.0000, 0.1302, 0.2604, 0.1294, 0.12…
## $ CAUS <dbl> 0.0000, 0.1506, 0.0000, 0.0000, 0.0000, 0.0000, 0.1294, 0.00…
## $ CC <dbl> 0.0000, 1.6566, 2.3677, 1.9152, 2.6042, 2.3438, 2.1992, 1.93…
## $ CD <dbl> 4.4944, 0.9036, 0.9749, 1.6416, 0.9115, 1.3021, 1.0349, 1.29…
## $ CONC <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ COND <dbl> 0.0000, 0.0000, 0.1393, 0.1368, 0.1302, 0.0000, 0.0000, 0.00…
## $ CONJ <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ DEMO <dbl> 0.0000, 0.0000, 0.5571, 0.5472, 1.0417, 0.3906, 0.3881, 0.64…
## $ DEMP <dbl> 0.0000, 0.0000, 0.8357, 0.6840, 0.0000, 0.1302, 0.2587, 0.00…
## $ DPAR <dbl> 0.0000, 0.0000, 0.4178, 0.1368, 0.2604, 0.1302, 0.5175, 0.00…
## $ DT <dbl> 0.0000, 9.7892, 10.4457, 11.2175, 11.7188, 12.2396, 11.3842,…
## $ DWNT <dbl> 0.0000, 0.3012, 0.1393, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ EMPH <dbl> 0.0000, 0.1506, 0.5571, 0.5472, 0.2604, 0.2604, 0.0000, 0.25…
## $ EX <dbl> 0.0000, 0.1506, 0.0000, 0.4104, 0.0000, 0.0000, 0.0000, 0.00…
## $ FPP1 <dbl> 0.0000, 0.4518, 1.3928, 0.6840, 0.2604, 0.2604, 0.1294, 0.12…
## $ FW <dbl> 4.4944, 0.4518, 0.0000, 0.1368, 1.3021, 0.0000, 0.5175, 0.25…
## $ GER <dbl> 0.0000, 1.5060, 0.1393, 0.0000, 0.1302, 0.0000, 0.0000, 0.38…
## $ HDG <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.12…
## $ IN <dbl> 0.0000, 0.9036, 1.6713, 2.0520, 1.4323, 1.9531, 1.6818, 1.54…
## $ INPR <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ JJ <dbl> 0.0000, 5.1205, 5.1532, 3.1464, 3.1250, 1.9531, 2.5873, 5.03…
## $ LS <dbl> 0.0000, 0.0000, 2.3677, 4.1040, 3.3854, 3.5156, 3.6223, 2.70…
## $ NEMD <dbl> 0.0000, 0.1506, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ NN <dbl> 91.0112, 38.1024, 32.4513, 28.4542, 30.0781, 32.5521, 26.261…
## $ NNP <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ NOMZ <dbl> 0.0000, 0.4518, 0.8357, 2.3256, 0.2604, 1.3021, 3.6223, 0.77…
## $ OSUB <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ PDT <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ PHC <dbl> 0.0000, 1.0542, 1.2535, 1.2312, 1.3021, 0.9115, 1.0349, 1.16…
## $ PIN <dbl> 0.0000, 9.9398, 10.1671, 8.0711, 8.0729, 11.4583, 7.6326, 12…
## $ PIT <dbl> 0.0000, 1.5060, 0.5571, 0.4104, 0.7812, 0.5208, 0.9056, 1.03…
## $ PLACE <dbl> 0.0000, 1.6566, 0.1393, 0.1368, 0.2604, 0.0000, 0.1294, 0.12…
## $ POMD <dbl> 0.0000, 0.7530, 0.1393, 0.9576, 1.1719, 1.4323, 1.4230, 0.77…
## $ POS <dbl> 0.0000, 0.9036, 1.2535, 0.8208, 0.3906, 1.3021, 0.7762, 0.77…
## $ PRED <dbl> 0.0000, 0.9036, 0.2786, 0.1368, 0.7812, 0.1302, 0.2587, 0.12…
## $ PRMD <dbl> 0.0000, 0.0000, 0.0000, 0.1368, 0.0000, 0.2604, 0.0000, 0.00…
## $ PRP <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ PRPS <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ QUAN <dbl> 0.0000, 0.9036, 0.2786, 0.2736, 0.5208, 0.2604, 0.0000, 0.38…
## $ QUPR <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2604, 0.0000, 0.12…
## $ RB <dbl> 0.0000, 1.8072, 1.3928, 1.9152, 1.6927, 0.9115, 2.8461, 2.19…
## $ RP <dbl> 0.0000, 0.1506, 0.2786, 0.6840, 0.2604, 0.0000, 0.0000, 0.25…
## $ SPP2 <dbl> 0.0000, 0.6024, 3.8997, 6.8399, 5.4688, 6.7708, 6.7270, 4.51…
## $ SYM <dbl> 0.0000, 1.5060, 0.0000, 0.0000, 0.5208, 0.0000, 0.1294, 0.25…
## $ SYNE <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ THAC <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ THVC <dbl> 0.0000, 0.1506, 0.0000, 0.0000, 0.0000, 0.1302, 0.0000, 0.00…
## $ TIME <dbl> 0.0000, 0.3012, 0.4178, 0.5472, 0.1302, 0.2604, 0.1294, 0.12…
## $ TO <dbl> 0.0000, 1.0542, 0.1393, 0.0000, 0.5208, 0.2604, 1.0349, 0.64…
## $ TOBJ <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ TPP3 <dbl> 0.0000, 2.4096, 1.6713, 2.0520, 0.9115, 1.1719, 1.2937, 1.29…
## $ TSUB <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1302, 0.0000, 0.00…
## $ UH <dbl> 0.0000, 0.0000, 0.0000, 0.1368, 0.0000, 0.1302, 0.0000, 0.00…
## $ VB <dbl> 0.0000, 3.0120, 11.6992, 12.1751, 11.5885, 10.8073, 13.0660,…
## $ VBD <dbl> 0.0000, 3.1627, 0.2786, 0.0000, 0.0000, 0.0000, 0.0000, 0.64…
## $ VBG <dbl> 0.0000, 0.3012, 0.1393, 0.1368, 0.5208, 0.0000, 0.6468, 0.00…
## $ VBN <dbl> 0.0000, 0.3012, 0.0000, 0.0000, 0.1302, 0.0000, 0.0000, 0.38…
## $ VPRT <dbl> 0.0000, 6.3253, 4.1783, 4.6512, 5.3385, 3.5156, 4.9159, 3.48…
## $ WDT <dbl> 0.0000, 0.0000, 0.2786, 0.1368, 0.5208, 0.3906, 1.0349, 0.25…
## $ WP <dbl> 0.0000, 0.3012, 1.2535, 1.2312, 1.4323, 1.5625, 1.6818, 1.80…
## $ WPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ XX0 <dbl> 0.0000, 0.6024, 0.8357, 0.0000, 1.0417, 0.1302, 0.5175, 0.12…
## $ X.BEMA. <dbl> 0.0000, 2.8614, 1.3928, 1.3680, 1.3021, 1.1719, 1.2937, 1.16…
## $ X.BYPA. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ X.CONT. <dbl> 0.0000, 0.6024, 1.3928, 0.6840, 1.9531, 0.5208, 1.0349, 0.12…
## $ X.PASS. <dbl> 0.0000, 0.3012, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ X.PASTP. <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ X.PEAS. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ X.PIRE. <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1294, 0.00…
## $ X.PRESP. <dbl> 0.0000, 0.1506, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ X.PRIV. <dbl> 0.0000, 1.2048, 1.6713, 2.3256, 2.0833, 2.3438, 2.1992, 2.58…
## $ X.PROD. <dbl> 0.0000, 0.3012, 0.0000, 0.0000, 0.2604, 0.1302, 0.5175, 0.77…
## $ X.PUBV. <dbl> 0.0000, 0.1506, 2.2284, 3.1464, 1.9531, 1.4323, 1.9405, 1.67…
## $ X.SERE. <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1294, 0.00…
## $ X.SMP. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ X.SPAU. <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ X.SPIN. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ X.STPR. <dbl> 0.0000, 0.4518, 0.1393, 0.0000, 0.0000, 0.2604, 0.2587, 0.00…
## $ X.SUAV. <dbl> 0.0000, 0.0000, 0.2786, 0.8208, 0.1302, 0.2604, 1.0349, 0.25…
## $ X.THATD. <dbl> 0.0000, 0.0000, 0.0000, 0.1368, 0.1302, 0.2604, 0.0000, 0.00…
## $ X.WHCL. <dbl> 0.0000, 0.1506, 0.2786, 0.2736, 0.5208, 0.1302, 0.6468, 0.38…
## $ X.WHOBJ. <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.00…
## $ X.WHQU. <dbl> 0.0000, 0.0000, 0.0000, 0.2736, 0.3906, 0.3906, 0.0000, 0.00…
## $ X.WHSUB. <dbl> 0.0000, 0.0000, 0.1393, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ X.WZPAST. <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.00…
## $ X.WZPRES. <dbl> 0.0000, 0.1506, 0.0000, 0.1368, 0.1302, 0.0000, 0.2587, 0.00…
## $ X...1 <dbl> 0.0000, 0.6024, 0.0000, 0.2736, 0.2604, 0.2604, 0.0000, 0.25…nrow(TxBcounts)## [1] 1950TxBcounts <- TxBcounts %>% filter(Filename!="._Access_1_Informative_0001.txt")
TxBcounts$Register <- as.factor(stringr::str_extract(TxBcounts$Filename, "Spoken|Narrative|Other|Personal|Informative|Instructional|Poetry")) # Add a variable for Textbook Register
summary(TxBcounts$Register)## Informative Instructional Narrative Personal Poetry
## 363 647 285 88 37
## Spoken
## 529TxBcounts$Register <-car::recode(TxBcounts$Register, "'Narrative' = 'Fiction'; 'Spoken' = 'Conversation'")
colnames(TxBcounts)## [1] "Filename" "Level" "Tokens" "AWL" "TTR" "X."
## [7] "X..1" "X.." "X..2" "X.LRB." "X.RRB." "."
## [13] "X..3" "AMP" "ANDC" "CAUS" "CC" "CD"
## [19] "CONC" "COND" "CONJ" "DEMO" "DEMP" "DPAR"
## [25] "DT" "DWNT" "EMPH" "EX" "FPP1" "FW"
## [31] "GER" "HDG" "IN" "INPR" "JJ" "LS"
## [37] "NEMD" "NN" "NNP" "NOMZ" "OSUB" "PDT"
## [43] "PHC" "PIN" "PIT" "PLACE" "POMD" "POS"
## [49] "PRED" "PRMD" "PRP" "PRPS" "QUAN" "QUPR"
## [55] "RB" "RP" "SPP2" "SYM" "SYNE" "THAC"
## [61] "THVC" "TIME" "TO" "TOBJ" "TPP3" "TSUB"
## [67] "UH" "VB" "VBD" "VBG" "VBN" "VPRT"
## [73] "WDT" "WP" "WPS" "XX0" "X.BEMA." "X.BYPA."
## [79] "X.CONT." "X.PASS." "X.PASTP." "X.PEAS." "X.PIRE." "X.PRESP."
## [85] "X.PRIV." "X.PROD." "X.PUBV." "X.SERE." "X.SMP." "X.SPAU."
## [91] "X.SPIN." "X.STPR." "X.SUAV." "X.THATD." "X.WHCL." "X.WHOBJ."
## [97] "X.WHQU." "X.WHSUB." "X.WZPAST." "X.WZPRES." "X...1" "Register"MAT_features <- read.csv(here("metadata", "MAT_features_TxB.csv"), header = F)
MAT_features <- MAT_features$V3 # Feature names without any spaces or hythens
MAT_features## [1] "Filename" "Level"
## [3] "Tokens" "AWL"
## [5] "TTR" "Hashtag"
## [7] "Dollar" "Quotation"
## [9] "Comma" "LeftBracket"
## [11] "RightBracket" "Full_stop"
## [13] "Colon" "Amplifiers"
## [15] "Ind_clause_coord" "because"
## [17] "Coord_conj" "Card_numb"
## [19] "although_though" "if_unless"
## [21] "Conjuncts" "Demonstratives"
## [23] "Dem_pronouns" "Discourse_particles"
## [25] "Determiners" "Downtoners"
## [27] "Emphatics" "Existential_there"
## [29] "FPP1" "Foreign_words"
## [31] "Gerunds" "Hedges"
## [33] "Prep_subord_conj" "Ind_pronouns"
## [35] "Attr_Adj" "List_markers"
## [37] "Necessity_modals" "Nouns"
## [39] "Proper_nouns" "Nominalizations"
## [41] "Adv_sub" "Pre_determiners"
## [43] "Phrasal_coord" "Prepositions"
## [45] "it" "Place_adv"
## [47] "Poss_modals" "Poss_S"
## [49] "Pred_adj" "Pred_modals"
## [51] "Personal_pronouns" "Poss_pronouns"
## [53] "Quantifiers" "Quantifying_pronouns"
## [55] "Adverbs" "Particles"
## [57] "SPP2" "Symbols"
## [59] "Syn_negation" "That_adj_comp"
## [61] "That_verb_comp" "Time_adv"
## [63] "To_infinitives" "That_rel_clause_obj"
## [65] "TPP3" "That_rel_clause_subj"
## [67] "Interjection" "Verb_base_form"
## [69] "Past_tense" "Verb_ing_form"
## [71] "Verb_past_par" "Present_tense"
## [73] "WH_determiner" "WH_pronoun"
## [75] "Poss_WH_pronoun" "Analytic_negation"
## [77] "BE_main_verb" "By_passives"
## [79] "Contractions" "Agentless_passives"
## [81] "Past_participial_clauses" "Perfect_aspect"
## [83] "Pied_piping_relative_clauses" "Present_part_clauses"
## [85] "Private_verbs" "Pro_verb_DO"
## [87] "Public_verbs" "Sentence_relatives"
## [89] "seem_appear" "Split_auxiliaries"
## [91] "Split_infinitives" "Stranded_prepositions"
## [93] "Suasive_verbs" "That_deletion"
## [95] "WH_clauses" "WH_rel_clauses_obj"
## [97] "Direct_WH_questions" "WH_rel_clauses_subj"
## [99] "Past_part_WHIZ_del_rel" "Present_part_WHIZ_del_rel"
## [101] "Quotation2" "Register"colnames(TxBcounts) <- MAT_features
colnames(TxBcounts)## [1] "Filename" "Level"
## [3] "Tokens" "AWL"
## [5] "TTR" "Hashtag"
## [7] "Dollar" "Quotation"
## [9] "Comma" "LeftBracket"
## [11] "RightBracket" "Full_stop"
## [13] "Colon" "Amplifiers"
## [15] "Ind_clause_coord" "because"
## [17] "Coord_conj" "Card_numb"
## [19] "although_though" "if_unless"
## [21] "Conjuncts" "Demonstratives"
## [23] "Dem_pronouns" "Discourse_particles"
## [25] "Determiners" "Downtoners"
## [27] "Emphatics" "Existential_there"
## [29] "FPP1" "Foreign_words"
## [31] "Gerunds" "Hedges"
## [33] "Prep_subord_conj" "Ind_pronouns"
## [35] "Attr_Adj" "List_markers"
## [37] "Necessity_modals" "Nouns"
## [39] "Proper_nouns" "Nominalizations"
## [41] "Adv_sub" "Pre_determiners"
## [43] "Phrasal_coord" "Prepositions"
## [45] "it" "Place_adv"
## [47] "Poss_modals" "Poss_S"
## [49] "Pred_adj" "Pred_modals"
## [51] "Personal_pronouns" "Poss_pronouns"
## [53] "Quantifiers" "Quantifying_pronouns"
## [55] "Adverbs" "Particles"
## [57] "SPP2" "Symbols"
## [59] "Syn_negation" "That_adj_comp"
## [61] "That_verb_comp" "Time_adv"
## [63] "To_infinitives" "That_rel_clause_obj"
## [65] "TPP3" "That_rel_clause_subj"
## [67] "Interjection" "Verb_base_form"
## [69] "Past_tense" "Verb_ing_form"
## [71] "Verb_past_par" "Present_tense"
## [73] "WH_determiner" "WH_pronoun"
## [75] "Poss_WH_pronoun" "Analytic_negation"
## [77] "BE_main_verb" "By_passives"
## [79] "Contractions" "Agentless_passives"
## [81] "Past_participial_clauses" "Perfect_aspect"
## [83] "Pied_piping_relative_clauses" "Present_part_clauses"
## [85] "Private_verbs" "Pro_verb_DO"
## [87] "Public_verbs" "Sentence_relatives"
## [89] "seem_appear" "Split_auxiliaries"
## [91] "Split_infinitives" "Stranded_prepositions"
## [93] "Suasive_verbs" "That_deletion"
## [95] "WH_clauses" "WH_rel_clauses_obj"
## [97] "Direct_WH_questions" "WH_rel_clauses_subj"
## [99] "Past_part_WHIZ_del_rel" "Present_part_WHIZ_del_rel"
## [101] "Quotation2" "Register"TxBcounts %>%
group_by(Register) %>%
summarise(totaltexts = n(), totalwords = sum(Tokens), meannouncount = mean(Nouns), sdnouncount = sd(Nouns), TTRmean = mean(TTR))## # A tibble: 6 × 6
## Register totaltexts totalwords meannouncount sdnouncount TTRmean
## <fct> <int> <int> <dbl> <dbl> <dbl>
## 1 Conversation 529 407591 24.4 4.34 0.459
## 2 Fiction 285 205072 22.2 3.82 0.498
## 3 Informative 363 265224 27.9 4.80 0.542
## 4 Instructional 647 499324 26.6 2.87 0.443
## 5 Personal 88 58534 22.5 4.10 0.511
## 6 Poetry 37 22358 23.4 4.74 0.465### Z-scores ###
TxBzscores <- read.delim(here("data", "TxB900MDA_zscores.tsv"), header = TRUE)
glimpse(TxBzscores)## Rows: 1,951
## Columns: 71
## $ Filename <chr> "._Access_1_Informative_0001.txt", "Access_1_Informative_000…
## $ Level <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", …
## $ AMP <dbl> -1.0385, 2.4369, -1.0385, -1.0385, -1.0385, -1.0385, -0.5408…
## $ ANDC <dbl> -0.9375, -0.6238, -0.3571, -0.9375, -0.6663, -0.3950, -0.667…
## $ AWL <dbl> -7.4579, -0.6099, -1.1142, -0.6036, -1.4062, -1.3053, -1.059…
## $ CAUS <dbl> -0.6471, 0.2388, -0.6471, -0.6471, -0.6471, -0.6471, 0.1141,…
## $ CONC <dbl> -0.625, -0.625, -0.625, -0.625, -0.625, -0.625, -0.625, -0.6…
## $ COND <dbl> -1.1364, -1.1364, -0.5032, -0.5145, -0.5445, -1.1364, -1.136…
## $ CONJ <dbl> -0.7500, -0.7500, -0.7500, -0.7500, -0.7500, -0.7500, -0.750…
## $ DEMO <dbl> -2.3571, -2.3571, -1.0307, -1.0543, 0.1231, -1.4271, -1.4331…
## $ DEMP <dbl> -0.9583, -0.9583, 0.7827, 0.4667, -0.9583, -0.6871, -0.4194,…
## $ DPAR <dbl> -0.5217, -0.5217, 1.2948, 0.0730, 0.6104, 0.0443, 1.7283, -0…
## $ DWNT <dbl> -1.2500, 0.6325, -0.3794, -1.2500, -1.2500, -1.2500, -1.2500…
## $ EMPH <dbl> -1.5000, -1.1414, -0.1736, -0.1971, -0.8800, -0.8800, -1.500…
## $ EX <dbl> -1.2222, -0.3856, -1.2222, 1.0578, -1.2222, -1.2222, -1.2222…
## $ FPP1 <dbl> -1.0421, -0.8690, -0.5085, -0.7801, -0.9424, -0.9424, -0.992…
## $ GER <dbl> -1.8421, 2.1211, -1.4755, -1.8421, -1.4995, -1.8421, -1.8421…
## $ HDG <dbl> -0.4615, -0.4615, -0.4615, -0.4615, -0.4615, -0.4615, -0.461…
## $ INPR <dbl> -0.7000, -0.7000, -0.7000, -0.7000, -0.7000, -0.7000, -0.700…
## $ JJ <dbl> -3.2287, -0.5051, -0.4877, -1.5551, -1.5665, -2.1898, -1.852…
## $ NEMD <dbl> -1.0000, -0.2829, -1.0000, -1.0000, -1.0000, -1.0000, -1.000…
## $ NN <dbl> 20.4947, 5.6327, 4.0453, 2.9225, 3.3787, 4.0736, 2.3065, 2.9…
## $ NOMZ <dbl> -1.3819, -1.0682, -0.8016, 0.2331, -1.2011, -0.4777, 1.1335,…
## $ OSUB <dbl> -0.9091, -0.9091, -0.9091, -0.9091, -0.9091, -0.9091, -0.909…
## $ PHC <dbl> -1.2593, 2.6452, 3.3833, 3.3007, 3.5633, 2.1167, 2.5737, 3.0…
## $ PIN <dbl> -4.3504, -0.4371, -0.3476, -1.1728, -1.1721, 0.1607, -1.3454…
## $ PIT <dbl> -1.4507, 0.6704, -0.6661, -0.8727, -0.3504, -0.7172, -0.1752…
## $ PLACE <dbl> -0.9118, 3.9606, -0.5021, -0.5094, -0.1459, -0.9118, -0.5312…
## $ POMD <dbl> -1.6571, 0.4943, -1.2591, 1.0789, 1.6911, 2.4351, 2.4086, 0.…
## $ PRED <dbl> -1.8077, 1.6677, -0.7362, -1.2815, 1.1969, -1.3069, -0.8127,…
## $ PRMD <dbl> -1.3333, -1.3333, -1.3333, -1.0076, -1.3333, -0.7133, -1.333…
## $ RB <dbl> -3.7273, -2.7005, -2.9359, -2.6391, -2.7655, -3.2094, -2.110…
## $ SPP2 <dbl> -0.7174, -0.2809, 2.1085, 4.2391, 3.2455, 4.1890, 4.1572, 2.…
## $ SYNE <dbl> -1.0625, -1.0625, -1.0625, -1.0625, -1.0625, -1.0625, -1.062…
## $ THAC <dbl> -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, …
## $ THVC <dbl> -1.1379, -0.6186, -1.1379, -1.1379, -1.1379, -0.6890, -1.137…
## $ TIME <dbl> -1.4857, -0.6251, -0.2920, 0.0777, -1.1137, -0.7417, -1.1160…
## $ TO <dbl> -2.6607, -0.7782, -2.4120, -2.6607, -1.7307, -2.1957, -0.812…
## $ TOBJ <dbl> -0.7273, -0.7273, -0.7273, -0.7273, -0.7273, -0.7273, -0.727…
## $ TPP3 <dbl> -1.3289, -0.2580, -0.5861, -0.4169, -0.9238, -0.8080, -0.753…
## $ TSUB <dbl> -0.5000, -0.5000, -0.5000, -0.5000, -0.5000, 1.1275, -0.5000…
## $ TTR <dbl> -6.1536, 0.4615, -2.8558, -2.1827, -2.3750, -2.9038, -1.7500…
## $ VBD <dbl> -1.3191, -0.2787, -1.2274, -1.3191, -1.3191, -1.3191, -1.319…
## $ VPRT <dbl> -2.2653, -0.4212, -1.0471, -0.9093, -0.7089, -1.2403, -0.832…
## $ XX0 <dbl> -1.3934, -0.4059, -0.0234, -1.3934, 0.3143, -1.1800, -0.5451…
## $ X.BEMA. <dbl> -2.9789, 0.0331, -1.5128, -1.5389, -1.6083, -1.7454, -1.6172…
## $ X.BYPA. <dbl> -0.6154, -0.6154, -0.6154, -0.6154, -0.6154, -0.6154, -0.615…
## $ X.CONT. <dbl> -0.7258, -0.4019, 0.0230, -0.3581, 0.3242, -0.4458, -0.1694,…
## $ X.PASS. <dbl> -1.4545, -0.9982, -1.4545, -1.4545, -1.4545, -1.4545, -1.454…
## $ X.PASTP. <dbl> -0.250, -0.250, -0.250, -0.250, -0.250, -0.250, -0.250, -0.2…
## $ X.PEAS. <dbl> -1.6538, -1.6538, -1.6538, -1.6538, -1.6538, -1.6538, -1.653…
## $ X.PIRE. <dbl> -0.6364, -0.6364, -0.6364, -0.6364, -0.6364, -0.6364, 0.5400…
## $ X.PRESP. <dbl> -0.5882, 0.2976, -0.5882, -0.5882, -0.5882, -0.5882, -0.5882…
## $ X.PRIV. <dbl> -1.7308, -0.5723, -0.1238, 0.5054, 0.2724, 0.5229, 0.3838, 0…
## $ X.PROD. <dbl> -0.8571, 0.0034, -0.8571, -0.8571, -0.1131, -0.4851, 0.6214,…
## $ X.PUBV. <dbl> -1.4259, -1.1470, 2.7007, 4.4007, 2.1909, 1.2265, 2.1676, 1.…
## $ X.SERE. <dbl> -0.2500, -0.2500, -0.2500, -0.2500, -0.2500, -0.2500, 2.9850…
## $ X.SMP. <dbl> -0.8, -0.8, -0.8, -0.8, -0.8, -0.8, -0.8, -0.8, -0.8, -0.8, …
## $ X.SPAU. <dbl> -2.2000, -2.2000, -2.2000, -2.2000, -2.2000, -2.2000, -2.200…
## $ X.SPIN. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ X.STPR. <dbl> -0.7407, 0.9326, -0.2248, -0.7407, -0.7407, 0.2237, 0.2174, …
## $ X.SUAV. <dbl> -0.9355, -0.9355, -0.0368, 1.7123, -0.5155, -0.0955, 2.4029,…
## $ X.THATD. <dbl> -0.7561, -0.7561, -0.7561, -0.4224, -0.4385, -0.1210, -0.756…
## $ X.WHCL. <dbl> -0.600, 0.906, 2.186, 2.136, 4.608, 0.702, 5.868, 3.271, 5.9…
## $ X.WHOBJ. <dbl> -0.8235, -0.8235, -0.8235, -0.8235, -0.8235, -0.8235, -0.823…
## $ X.WHQU. <dbl> -0.3333, -0.3333, -0.3333, 4.2267, 6.1767, 6.1767, -0.3333, …
## $ X.WHSUB. <dbl> -1.0500, -1.0500, -0.3535, -1.0500, -1.0500, -1.0500, -1.050…
## $ X.WZPAST. <dbl> -0.8065, -0.8065, -0.8065, -0.8065, -0.8065, -0.8065, -0.806…
## $ X.WZPRES. <dbl> -0.8889, -0.0522, -0.8889, -0.1289, -0.1656, -0.8889, 0.5483…
## $ Underused <chr> "AWL DEMO JJ PIN RB TO TTR VPRT [BEMA] [SPAU]", "DEMO RB [SP…
## $ Overused <chr> "NN", "AMP GER NN PHC PLACE", "NN PHC SPP2 [PUBV] [WHCL]", "…TxBzscores <- TxBzscores %>% filter(Filename!="CORPUS" & Filename!="._Access_1_Informative_0001.txt") # Remove this row with mean values for the corpus
nrow(TxBzscores)## [1] 1949colnames(TxBzscores)## [1] "Filename" "Level" "AMP" "ANDC" "AWL" "CAUS"
## [7] "CONC" "COND" "CONJ" "DEMO" "DEMP" "DPAR"
## [13] "DWNT" "EMPH" "EX" "FPP1" "GER" "HDG"
## [19] "INPR" "JJ" "NEMD" "NN" "NOMZ" "OSUB"
## [25] "PHC" "PIN" "PIT" "PLACE" "POMD" "PRED"
## [31] "PRMD" "RB" "SPP2" "SYNE" "THAC" "THVC"
## [37] "TIME" "TO" "TOBJ" "TPP3" "TSUB" "TTR"
## [43] "VBD" "VPRT" "XX0" "X.BEMA." "X.BYPA." "X.CONT."
## [49] "X.PASS." "X.PASTP." "X.PEAS." "X.PIRE." "X.PRESP." "X.PRIV."
## [55] "X.PROD." "X.PUBV." "X.SERE." "X.SMP." "X.SPAU." "X.SPIN."
## [61] "X.STPR." "X.SUAV." "X.THATD." "X.WHCL." "X.WHOBJ." "X.WHQU."
## [67] "X.WHSUB." "X.WZPAST." "X.WZPRES." "Underused" "Overused"MAT_features <- read.csv(here("metadata", "MAT_features_zscores.csv"), header = F)
MAT_features <- MAT_features$V3 # Feature names without any spaces or hythens
colnames(TxBzscores) <- MAT_features
colnames(TxBzscores)## [1] "Filename" "Level"
## [3] "Amplifiers" "Ind_clause_coord"
## [5] "AWL" "because"
## [7] "Coord_conj" "if_unless"
## [9] "although_though" "Demonstratives"
## [11] "Dem_pronouns" "Discourse_particles"
## [13] "Downtoners" "Emphatics"
## [15] "Existential_there" "FPP1"
## [17] "Gerunds" "Hedges"
## [19] "Ind_pronouns" "Attr_Adj"
## [21] "Necessity_modals" "Nouns"
## [23] "Nominalizations" "Adv_sub"
## [25] "Phrasal_coord" "Prepositions"
## [27] "it" "Place_adv"
## [29] "Poss_modals" "Pred_adj"
## [31] "Pred_modals" "Adverbs"
## [33] "SPP2" "Syn_negation"
## [35] "That_adj_comp" "That_verb_comp"
## [37] "Time_adv" "To_infinitives"
## [39] "That_rel_clause_obj" "TPP3"
## [41] "That_rel_clause_subj" "TTR"
## [43] "Past_tense" "Present_tense"
## [45] "Analytic_negation" "BE_main_verb"
## [47] "By_passives" "Contractions"
## [49] "Agentless_passives" "Past_participial_clauses"
## [51] "Perfect_aspect" "Pied_piping_relative_clauses"
## [53] "Present_part_clauses" "Private_verbs"
## [55] "Pro_verb_DO" "Public_verbs"
## [57] "Sentence_relatives" "seem_appear"
## [59] "Split_auxiliaries" "Split_infinitives"
## [61] "Stranded_prepositions" "Suasive_verbs"
## [63] "That_deletion" "WH_clauses"
## [65] "WH_rel_clauses_obj" "Direct_WH_questions"
## [67] "WH_rel_clauses_subj" "Past_part_WHIZ_del_rel"
## [69] "Present_part_WHIZ_del_rel" "Underused"
## [71] "Overused"TxBzscores$Register <- as.factor(stringr::str_extract(TxBzscores$Filename, "Spoken|Narrative|Other|Personal|Informative|Instructional|Poetry")) # Add a variable for Textbook Register
TxBzscores$Register <-car::recode(TxBzscores$Register, "'Narrative' = 'Fiction'; 'Spoken' = 'Conversation'")
summary(TxBzscores$Register)## Conversation Fiction Informative Instructional Personal
## 529 285 363 647 88
## Poetry
## 37# Textbook Series variable
TxBzscores$Filename <- stringr::str_replace(TxBzscores$Filename, "English_In_Mind", "EIM")
TxBzscores$Filename <- stringr::str_replace(TxBzscores$Filename, "English_in_Mind", "EIM") # This is necessary because of inconsistencies in the use of capital letters for this series
TxBzscores$Filename <- stringr::str_replace(TxBzscores$Filename, "New_GreenLine", "NGL") # Otherwise the regex for GreenLine will override New_GreenLine
TxBzscores$Filename <- stringr::str_replace(TxBzscores$Filename, "Piece_of_cake", "POC") # Otherwise the regex for GreenLine will override New_GreenLine
TxBzscores$Series <- as.factor(stringr::str_extract(TxBzscores$Filename, "Access|Achievers|EIM|GreenLine|HT|NB|NM|POC|JTT|NGL|Solutions"))
summary(TxBzscores$Series)## Access Achievers EIM GreenLine HT JTT NB NGL
## 282 238 174 206 114 130 45 300
## NM POC Solutions
## 58 92 310TxBzscores$Series <-car::recode(TxBzscores$Series, "c('NB', 'JTT') = 'JTT'; c('NM', 'HT') = 'HT'")
# Textbook country variable
TxBzscores$Country <- TxBzscores$Series
TxBzscores$Country <- car::recode(TxBzscores$Series, "c('Access', 'GreenLine', 'NGL') = 'Germany'; c('Achievers', 'EIM', 'Solutions') = 'Spain'; c('HT', 'NB', 'NM', 'POC', 'JTT') = 'France'")
summary(TxBzscores$Country)## France Germany Spain
## 439 788 722TxBzscores$Level <- as.factor(TxBzscores$Level)TxBdimensions <- read.delim(here("data", "TxB900MDA_dims.tsv"), header = TRUE)
filter(TxBdimensions, Filename=="CORPUS") # This row reports the mean values for each dimension## Filename Level Dim1 Dim2 Dim3 Dim4 Dim5 Dim6
## 1 CORPUS <NA> 3.051 -1.6558 1.5119 -1.6541 -1.953 -1.6102
## TextType
## 1 Involved persuasionTxBdimensions <- TxBdimensions %>% filter(Filename!="CORPUS") # Remove the row with mean values
glimpse(TxBdimensions)## Rows: 1,949
## Columns: 9
## $ Filename <chr> "Access_1_Informative_0001.txt", "Access_1_Instructional_0001…
## $ Level <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "…
## $ Dim1 <dbl> -7.3851, -4.9261, -2.4381, -0.1935, -1.6453, 4.4156, -6.7901,…
## $ Dim2 <dbl> -4.1024, -2.4173, -0.6398, -3.3565, -4.2051, -3.2099, -3.4863…
## $ Dim3 <dbl> -0.9315, 5.1347, 4.7311, 4.5138, 4.6284, 5.5911, 4.4546, 2.16…
## $ Dim4 <dbl> -6.6663, -7.4853, -5.6705, -7.3240, -7.3409, -4.0795, -7.2812…
## $ Dim5 <dbl> -3.4638, -3.9201, -3.9201, -3.9201, -3.9201, -3.9201, -3.9201…
## $ Dim6 <dbl> -2.9757, -2.1686, -2.1922, -1.0148, -2.1161, -2.5710, -1.9589…
## $ TextType <chr> "General narrative exposition", "General narrative exposition…# Register variable
TxBdimensions$Register <- as.factor(stringr::str_extract(TxBdimensions$Filename, "Spoken|Narrative|Other|Personal|Informative|Instructional|Poetry")) # Add a variable for Textbook Register
summary(TxBdimensions$Register)## Informative Instructional Narrative Personal Poetry
## 363 647 285 88 37
## Spoken
## 529TxBdimensions$Register <-car::recode(TxBdimensions$Register, "'Narrative' = 'Fiction'; 'Spoken' = 'Conversation'")
# Show me the Textbook Series variable for ease of plotting
TxBdimensions$Filename <- stringr::str_replace(TxBdimensions$Filename, "English_In_Mind", "EIM")
TxBdimensions$Filename <- stringr::str_replace(TxBdimensions$Filename, "English_in_Mind", "EIM") # This is necessary because of inconsistencies in the use of capital letters for this series
TxBdimensions$Filename <- stringr::str_replace(TxBdimensions$Filename, "New_GreenLine", "NGL") # Otherwise the regex for GreenLine will override New_GreenLine
TxBdimensions$Filename <- stringr::str_replace(TxBdimensions$Filename, "Piece_of_cake", "POC") # Otherwise the regex for GreenLine will override New_GreenLine
TxBdimensions$Series <- as.factor(stringr::str_extract(TxBdimensions$Filename, "Access|Achievers|EIM|GreenLine|HT|NB|NM|POC|JTT|NGL|Solutions"))
summary(TxBdimensions$Series)## Access Achievers EIM GreenLine HT JTT NB NGL
## 282 238 174 206 114 130 45 300
## NM POC Solutions
## 58 92 310# Match the level E French textbooks to their corresponding series
TxBdimensions$Series <-car::recode(TxBdimensions$Series, "c('NB', 'JTT') = 'JTT'; c('NM', 'HT') = 'HT'")
# Textbook country variable
TxBdimensions$Country <- TxBdimensions$Series
TxBdimensions$Country <- car::recode(TxBdimensions$Series, "c('Access', 'GreenLine', 'NGL') = 'Germany'; c('Achievers', 'EIM', 'Solutions') = 'Spain'; c('HT', 'NB', 'NM', 'POC', 'JTT') = 'France'")
summary(TxBdimensions$Country)## France Germany Spain
## 439 788 722TxBdimensions$Level <- as.factor(TxBdimensions$Level)Additive MDA: Intra-textbook analysis I
Dim1
Rainplots for all textbook registers on Dimension 1
ggplot(TxBdimensions,aes(x=Register,y=Dim1, fill = Register, colour = Register))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(position = position_nudge(x = .25), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 1 (Biber 1988)') + xlab('Textbook Registers') +
theme_cowplot() +
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 1: Involved vs. Informational Discourse")
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -30, to = 40, by = 10)) 
#ggsave(here("plots", "TxB_Reg_Dim1.svg"), width = 10, height = 6)
#ggsave(here("plots", "TxB_Reg_Dim1.png"), width = 10, height = 6, dpi = 300)Modelling Dim1 results: Linear regression with mixed effects
Checks
Predictors
Check the relationships among the predictor variables and the grouping variable.
# Plot 1
ggplot(TxBdimensions, aes(x = Level)) +
geom_bar() +
facet_grid(rows = vars(Series), cols = vars(Register))
# Plot 2 (more detailed)
fig_obs <- ggplot(TxBdimensions, aes(x = Level, y = Dim1)) +
geom_point(
shape = "circle filled",
fill = "grey",
position = position_jitter(width = 0.2, height = 0)
) +
facet_grid(rows = vars(Series), cols = vars(Register))
fig_obs
Basic observations: * There are relatively few ‘Personal’ and ‘Poetry’ texts. * Some series (‘POC’ and ‘Solutions’) have almost no fiction. * Some series (e.g. ‘POC’ and ‘Solutions’ again) have no level ‘E’ textbooks
Outcome
Check the distribution of the outcome variable. Note that the y axis varies in scale so as to ‘zoom in’ on the shape of the distribution for those registers that contain much fewer texts.
fig_outcome <- ggplot(TxBdimensions, aes(x = Dim1)) +
geom_histogram(bins = 20) +
facet_grid(rows = vars(Register), cols = vars(Level), scales = "free_y")
fig_outcome
Aside from the fact that some combinations of Register and Level contain very few texts, Dim1 looks approximately normally distributed in each.
Models and model comparisons
Data for Poetry is too sparse so I am excluding it from these models. Thus, the following models are for just four of the registers. Following Gries and others, I am following this method: 1. Fit baseline model with only random effect structure. 2. Fit all plausible models. 3. Check for (significant) differences between models.
TxBdim_nopoetry <- TxBdimensions %>% filter(Register != "Poetry") %>% droplevels(.)
summary(TxBdim_nopoetry$Register) # Check## Conversation Fiction Informative Instructional Personal
## 529 285 363 647 88md0 <- lmer(Dim1 ~ 1 + (1|Series), TxBdim_nopoetry, REML = FALSE)
md_register <- update(md0, .~. + Register)
md_level <- update(md0, . ~ . + Level)
md_both <- update(md_register, .~. + Level)
md_interaction <- update(md_both, . ~ . + Level:Register)
anova(md0, md_register, md_both, md_interaction)## Data: TxBdim_nopoetry
## Models:
## md0: Dim1 ~ 1 + (1 | Series)
## md_register: Dim1 ~ (1 | Series) + Register
## md_both: Dim1 ~ (1 | Series) + Register + Level
## md_interaction: Dim1 ~ (1 | Series) + Register + Level + Register:Level
## npar AIC BIC logLik deviance Chisq Df
## md0 3 14620 14637 -7307.2 14614
## md_register 7 12689 12728 -6337.7 12675 1939.016 4
## md_both 11 12633 12694 -6305.4 12611 64.466 4
## md_interaction 27 12622 12772 -6284.1 12568 42.627 16
## Pr(>Chisq)
## md0
## md_register < 0.00000000000000022 ***
## md_both 0.0000000000003333 ***
## md_interaction 0.0003179 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(md0, md_level)## Data: TxBdim_nopoetry
## Models:
## md0: Dim1 ~ 1 + (1 | Series)
## md_level: Dim1 ~ (1 | Series) + Level
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 3 14620 14637 -7307.2 14614
## md_level 7 14576 14615 -7281.1 14562 52.261 4 0.0000000001216 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1md_final <- lmer(Dim1 ~ (Register|Series) + Register + Level + Level:Register, TxBdim_nopoetry, REML = FALSE) # boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingulartab_model(md_final)| Dim 1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 16.41 | 14.35 – 18.47 | <0.001 |
| Register [Fiction] | -6.53 | -9.06 – -4.00 | <0.001 |
| Register [Informative] | -20.02 | -23.23 – -16.80 | <0.001 |
| Register [Instructional] | -21.21 | -23.70 – -18.73 | <0.001 |
| Register [Personal] | -5.87 | -9.69 – -2.05 | 0.003 |
| Level [B] | 0.30 | -1.38 – 1.98 | 0.723 |
| Level [C] | -0.41 | -2.08 – 1.26 | 0.631 |
| Level [D] | -2.23 | -3.92 – -0.54 | 0.010 |
| Level [E] | -1.68 | -3.61 – 0.25 | 0.088 |
|
Register [Fiction] * Level [B] |
-3.92 | -7.02 – -0.83 | 0.013 |
|
Register [Informative] * Level [B] |
-0.86 | -4.02 – 2.29 | 0.592 |
|
Register [Instructional] * Level [B] |
0.59 | -1.72 – 2.90 | 0.618 |
|
Register [Personal] * Level [B] |
-2.09 | -6.68 – 2.49 | 0.371 |
|
Register [Fiction] * Level [C] |
-4.33 | -7.44 – -1.21 | 0.006 |
|
Register [Informative] * Level [C] |
-0.45 | -3.47 – 2.57 | 0.771 |
|
Register [Instructional] * Level [C] |
1.36 | -0.92 – 3.65 | 0.242 |
|
Register [Personal] * Level [C] |
-0.81 | -5.47 – 3.84 | 0.732 |
|
Register [Fiction] * Level [D] |
-6.54 | -9.57 – -3.52 | <0.001 |
|
Register [Informative] * Level [D] |
-1.10 | -4.11 – 1.91 | 0.472 |
|
Register [Instructional] * Level [D] |
2.25 | -0.05 – 4.55 | 0.055 |
|
Register [Personal] * Level [D] |
0.06 | -4.82 – 4.94 | 0.982 |
|
Register [Fiction] * Level [E] |
-5.90 | -9.03 – -2.76 | <0.001 |
|
Register [Informative] * Level [E] |
-3.28 | -6.51 – -0.06 | 0.046 |
|
Register [Instructional] * Level [E] |
-0.07 | -2.62 – 2.48 | 0.957 |
|
Register [Personal] * Level [E] |
0.25 | -4.67 – 5.17 | 0.919 |
| Random Effects | |||
| σ2 | 38.31 | ||
| τ00 Series | 6.07 | ||
| τ11 Series.RegisterFiction | 1.03 | ||
| τ11 Series.RegisterInformative | 9.07 | ||
| τ11 Series.RegisterInstructional | 7.19 | ||
| τ11 Series.RegisterPersonal | 3.20 | ||
| ρ01 | -0.27 | ||
| 0.19 | |||
| -0.67 | |||
| 0.29 | |||
| ICC | 0.17 | ||
| N Series | 9 | ||
| Observations | 1912 | ||
| Marginal R2 / Conditional R2 | 0.645 / 0.706 | ||
Singularity
The model fit is singular.
The summary of the random effects can tell us more. It shows the variance-covariance matrix for the random effect estimates.
VarCorr(md_final)## Groups Name Std.Dev. Corr
## Series (Intercept) 2.4638
## RegisterFiction 1.0125 -0.274
## RegisterInformative 3.0117 0.194 -0.688
## RegisterInstructional 2.6812 -0.675 0.458 0.041
## RegisterPersonal 1.7891 0.288 -0.995 0.754 -0.435
## Residual 6.1896md_final_ranefs <- tidy(md_final, effects = "ran_vals")
head(md_final_ranefs)## # A tibble: 6 × 6
## effect group level term estimate std.error
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 ran_vals Series Access (Intercept) -0.842 0.461
## 2 ran_vals Series Achievers (Intercept) -1.03 0.742
## 3 ran_vals Series EIM (Intercept) 4.88 0.748
## 4 ran_vals Series GreenLine (Intercept) 1.25 0.588
## 5 ran_vals Series HT (Intercept) -2.04 0.646
## 6 ran_vals Series JTT (Intercept) -0.746 0.674To look at the association between intercepts and slopes, we need the values in the term column as separate columns.
md_final_ranefs %>%
pivot_wider(
id_cols = c(group, level),
names_from = term,
values_from = estimate
) ->
md_final_ranefs
head(md_final_ranefs)## # A tibble: 6 × 7
## group level `(Intercept)` RegisterFiction RegisterInformat… RegisterInstruc…
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Series Access -0.842 1.00 -0.0521 3.01
## 2 Series Achie… -1.03 -1.19 4.53 1.52
## 3 Series EIM 4.88 -0.800 0.781 -5.42
## 4 Series Green… 1.25 0.447 1.91 2.30
## 5 Series HT -2.04 1.34 -4.95 0.515
## 6 Series JTT -0.746 0.593 -4.01 -0.374
## # … with 1 more variable: RegisterPersonal <dbl>Now we can plot a matrix of correlations among the estimated random effects.
md_final_ranefs %>%
select(`(Intercept)`, starts_with("Register")) %>%
ggpairs(
lower = list(continuous = wrap("smooth_lm", shape = "circle filled", fill = "grey", se = FALSE)),
diag = "blank",
upper = list(continuous = wrap("cor", stars = FALSE))
)
The scatterplots show close linear relationships among some of the estimates. In particular, the estimates for the random effect of the ‘Personal’ register are closely correlated with ‘Fiction’ and ‘Informative,’ as was also the case in the estimated covariance matrix. Zoom in on this correlation to understand what each plot in the matrix represents.
fig_ranef_cor <- ggplot(md_final_ranefs,
aes(x = RegisterFiction, y = RegisterPersonal, label = level)) +
geom_point(shape = "bullet") +
geom_text(hjust = "inward")
fig_ranef_cor
In general, the higher the estimated Dim1 for Personal Communication texts in a given textbook series, the lower the estimated Dim1 for Fiction in that same series. Hence, in a certain sense, some of these random slopes for Register are providing ‘redundant’ information. If the random effect for one register is almost perfectly correlated with another, then there is no need for them both. Indeed, this redundancy can make it difficult to estimate both reliably.
Final Dim1 model
TxBdim_nopoetry <- TxBdimensions %>% filter(Register != "Poetry") %>% droplevels(.)
summary(TxBdim_nopoetry$Register) # Check## Conversation Fiction Informative Instructional Personal
## 529 285 363 647 88md_final <- lmer(Dim1 ~ (1|Series) + Register + Level + Level:Register, TxBdim_nopoetry, REML = FALSE) # no warnings
tab_model(md_final, wrap.labels = 100) | Dim 1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 16.34 | 14.41 – 18.26 | <0.001 |
| Register [Fiction] | -6.96 | -9.44 – -4.47 | <0.001 |
| Register [Informative] | -18.95 | -21.54 – -16.36 | <0.001 |
| Register [Instructional] | -21.42 | -23.23 – -19.62 | <0.001 |
| Register [Personal] | -5.29 | -9.03 – -1.56 | 0.006 |
| Level [B] | 0.62 | -1.12 – 2.36 | 0.485 |
| Level [C] | -0.28 | -2.01 – 1.45 | 0.753 |
| Level [D] | -2.19 | -3.94 – -0.44 | 0.014 |
| Level [E] | -1.27 | -3.25 – 0.71 | 0.208 |
| Register [Fiction] * Level [B] | -4.09 | -7.30 – -0.88 | 0.013 |
| Register [Informative] * Level [B] | -1.80 | -5.07 – 1.46 | 0.279 |
| Register [Instructional] * Level [B] | 0.21 | -2.19 – 2.60 | 0.866 |
| Register [Personal] * Level [B] | -2.82 | -7.56 – 1.91 | 0.243 |
| Register [Fiction] * Level [C] | -4.09 | -7.31 – -0.86 | 0.013 |
| Register [Informative] * Level [C] | -0.61 | -3.74 – 2.52 | 0.703 |
| Register [Instructional] * Level [C] | 1.20 | -1.17 – 3.57 | 0.321 |
| Register [Personal] * Level [C] | -1.53 | -6.34 – 3.29 | 0.534 |
| Register [Fiction] * Level [D] | -6.06 | -9.19 – -2.93 | <0.001 |
| Register [Informative] * Level [D] | -1.37 | -4.48 – 1.73 | 0.386 |
| Register [Instructional] * Level [D] | 2.21 | -0.17 – 4.60 | 0.069 |
| Register [Personal] * Level [D] | -0.46 | -5.49 – 4.56 | 0.856 |
| Register [Fiction] * Level [E] | -5.64 | -8.87 – -2.40 | 0.001 |
| Register [Informative] * Level [E] | -4.45 | -7.71 – -1.18 | 0.008 |
| Register [Instructional] * Level [E] | -0.24 | -2.83 – 2.35 | 0.857 |
| Register [Personal] * Level [E] | -0.53 | -5.58 – 4.52 | 0.836 |
| Random Effects | |||
| σ2 | 41.29 | ||
| τ00 Series | 4.60 | ||
| ICC | 0.10 | ||
| N Series | 9 | ||
| Observations | 1912 | ||
| Marginal R2 / Conditional R2 | 0.647 / 0.682 | ||
Checks
The basic checks look okay: * No warnings about problems with the fitting. * The variance of the random effects is not zero. * None of the standard errors are really huge (although those for the interactions involving the ‘Personal’ register are larger because data is sparse for this register).
# check distribution of residuals
plot(md_final)
# We can access the estimated deviation between each series average Dim1 1 and the overall average:
ranef(md_final)## $Series
## (Intercept)
## Access 0.04163842
## Achievers 0.82686763
## EIM 2.80050868
## GreenLine 2.41507553
## HT -2.81224079
## JTT -1.79858728
## NGL 2.35060266
## POC -2.68886659
## Solutions -1.13499826
##
## with conditional variances for "Series"Visualising fitted vs. observed values
TxBdim_nopoetry[, "predicted"] <- predict(md_final)
p <- ggplot(TxBdim_nopoetry, aes(x = Level, y = Dim1)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
facet_grid(rows = vars(Series), cols = vars(Register)) +
geom_point(aes(y = predicted), shape = "triangle filled", fill = "red")+
labs(y = "Dimension 1 (Biber 1988)", x = "Textbook Level")
top_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Register") +
theme_void()
right_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Series", angle = -90) +
theme_void()
blank_panel <- ggplot() +
theme_void()
jpeg(here("plots", "TxB_Dim1_Pred_Obs_1Series_RS.jpeg"), height = 30, width = 20, units = "cm", res = 300)
gridExtra::grid.arrange(
top_label, blank_panel, p, right_label,
nrow = 2, ncol = 2,
widths = c(30, 1), heights = c(1, 30)
)
#dev.off()
# Save plot using Rstudio export function (dimensions: 600 to 900 work well)Comparing groups
Register_results <- emmeans(md_final, "Register")## NOTE: Results may be misleading due to involvement in interactionssummary(Register_results)## Register emmean SE df lower.CL upper.CL
## Conversation 15.71 0.818 12.7 13.94 17.48
## Fiction 4.78 0.868 16.4 2.94 6.62
## Informative -4.89 0.850 15.0 -6.70 -3.07
## Instructional -5.04 0.806 11.9 -6.79 -3.28
## Personal 9.35 1.038 35.3 7.24 11.45
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95Level_results <- emmeans(md_final, "Level")## NOTE: Results may be misleading due to involvement in interactionssummary(Level_results)## Level emmean SE df lower.CL upper.CL
## A 5.81 0.920 21.2 3.898 7.72
## B 4.73 0.863 16.1 2.898 6.56
## C 4.53 0.863 16.0 2.699 6.36
## D 2.48 0.869 16.6 0.644 4.32
## E 2.37 0.877 17.1 0.519 4.22
##
## Results are averaged over the levels of: Register
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95comparisons <- pairs(Register_results, adjust = "tukey")
comparisons## contrast estimate SE df t.ratio p.value
## Conversation - Fiction 10.93 0.505 1936 21.645 <.0001
## Conversation - Informative 20.60 0.478 1933 43.096 <.0001
## Conversation - Instructional 20.75 0.394 1933 52.684 <.0001
## Conversation - Personal 6.36 0.763 1930 8.342 <.0001
## Fiction - Informative 9.67 0.564 1936 17.148 <.0001
## Fiction - Instructional 9.82 0.487 1936 20.151 <.0001
## Fiction - Personal -4.57 0.812 1930 -5.621 <.0001
## Informative - Instructional 0.15 0.452 1929 0.332 0.9974
## Informative - Personal -14.23 0.795 1928 -17.893 <.0001
## Instructional - Personal -14.38 0.748 1928 -19.233 <.0001
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 5 estimatesconfint(comparisons)## contrast estimate SE df lower.CL upper.CL
## Conversation - Fiction 10.93 0.505 1936 9.55 12.31
## Conversation - Informative 20.60 0.478 1933 19.29 21.90
## Conversation - Instructional 20.75 0.394 1933 19.67 21.82
## Conversation - Personal 6.36 0.763 1930 4.28 8.45
## Fiction - Informative 9.67 0.564 1936 8.13 11.21
## Fiction - Instructional 9.82 0.487 1936 8.49 11.15
## Fiction - Personal -4.57 0.812 1930 -6.78 -2.35
## Informative - Instructional 0.15 0.452 1929 -1.08 1.38
## Informative - Personal -14.23 0.795 1928 -16.40 -12.06
## Instructional - Personal -14.38 0.748 1928 -16.42 -12.34
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 5 estimatesVisualising effects
visreg(md_final, xvar = "Series", by="Register", type = "conditional", re.form=~(1|Series), line=list(col="darkred"), xlab = "Textbook Series", ylab = "Dimension 1 (Biber 1988)", layout=c(2,3))
Textual Analysis of individual features on Dim1
Dim1 <- TxBzscores %>%
dplyr::select(Register, Private_verbs, That_deletion, Contractions, Present_tense, SPP2, Pro_verb_DO, Analytic_negation, Dem_pronouns, Emphatics, FPP1, it, BE_main_verb, because, Discourse_particles, Ind_pronouns, Hedges, Amplifiers, Sentence_relatives, Direct_WH_questions, Poss_modals, Ind_clause_coord, WH_clauses, Stranded_prepositions, Nouns, AWL, Prepositions, TTR, Attr_Adj) %>%
group_by(Register) %>%
summarise_all(mean) %>%
mutate_if(is.numeric, round, digits = 2) %>%
t()
head(Dim1)## [,1] [,2] [,3] [,4] [,5]
## Register "Conversation" "Fiction" "Informative" "Instructional" "Personal"
## Private_verbs " 0.19" " 0.23" "-0.39" " 0.58" " 0.06"
## That_deletion " 0.3" " 0.0" "-0.3" "-0.2" " 0.1"
## Contractions " 1.56" " 0.47" "-0.19" "-0.46" " 1.13"
## Present_tense " 0.26" "-0.66" "-0.66" "-0.81" "-0.15"
## SPP2 "1.52" "0.35" "0.05" "2.55" "0.94"
## [,6]
## Register "Poetry"
## Private_verbs " 0.00"
## That_deletion " 0.1"
## Contractions " 1.23"
## Present_tense " 0.10"
## SPP2 "1.19"Dim2
Rainplots
# Rainplots for all textbook registers on Dimension 2
TxBdimensions %>%
group_by(Register) %>%
summarise(mean = mean(Dim2), sd = sd(Dim2)) %>%
mutate_if(is.numeric, round, digits = 2)## # A tibble: 6 × 3
## Register mean sd
## <fct> <dbl> <dbl>
## 1 Conversation -3.17 2.05
## 2 Fiction 2.53 2.83
## 3 Informative -1.51 2.78
## 4 Instructional -2.25 1.63
## 5 Personal -2.31 2.22
## 6 Poetry -1.57 4.21ggplot(TxBdimensions,aes(x=Register,y=Dim2, fill = Register, colour = Register))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
geom_boxplot(position = position_nudge(x = .25), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 2 (Biber 1988)')+xlab('Textbook Registers') +
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 2: Narrative vs. Non-narrative Concerns") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 20, by = 5)) 
#ggsave(here("plots", "TxB_Reg_Dim2.svg"), width = 10, height = 6)
# Consider filtering out the outlier in Poetry: Solutions_Elementary_Poetry_0001.txt
# Very high score due to repetition of "Ain't got no..." which is tagged as past tense verb (0.90) + synthetic negation (0.40), both of which score positively on factor 2.
# Removing clear outliner
TxBdimensions2 <- TxBdimensions %>% filter(Filename!="Solutions_Elementary_Poetry_0001.txt")
ggplot(TxBdimensions2,aes(x=Register,y=Dim2, fill = Register, colour = Register))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim2), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 2 (Biber 1988)')+xlab('Textbook Registers')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 2: Narrative vs. Non-narrative Concerns")
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 15, by = 5)) ## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
#ggsave(here("plots", "TxB_Reg_Dim2.svg"), width = 10, height = 6)LME model, plots and checks
md_final2 <- lmer(Dim2 ~ (1|Series) + Register + Level + Level:Register, TxBdim_nopoetry, REML = FALSE) # no warnings
tab_model(md_final2, wrap.labels = 100) | Dim 2 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -4.09 | -4.52 – -3.65 | <0.001 |
| Register [Fiction] | 3.95 | 3.14 – 4.76 | <0.001 |
| Register [Informative] | 0.71 | -0.13 – 1.56 | 0.097 |
| Register [Instructional] | 1.36 | 0.77 – 1.95 | <0.001 |
| Register [Personal] | 0.57 | -0.64 – 1.79 | 0.357 |
| Level [B] | 0.27 | -0.30 – 0.83 | 0.357 |
| Level [C] | 0.90 | 0.33 – 1.46 | 0.002 |
| Level [D] | 1.74 | 1.17 – 2.31 | <0.001 |
| Level [E] | 1.78 | 1.14 – 2.41 | <0.001 |
| Register [Fiction] * Level [B] | 2.79 | 1.74 – 3.84 | <0.001 |
| Register [Informative] * Level [B] | 0.69 | -0.37 – 1.76 | 0.203 |
| Register [Instructional] * Level [B] | -0.19 | -0.97 – 0.59 | 0.629 |
| Register [Personal] * Level [B] | -0.21 | -1.75 – 1.34 | 0.790 |
| Register [Fiction] * Level [C] | 2.71 | 1.66 – 3.77 | <0.001 |
| Register [Informative] * Level [C] | 0.77 | -0.25 – 1.79 | 0.140 |
| Register [Instructional] * Level [C] | -0.46 | -1.23 – 0.32 | 0.246 |
| Register [Personal] * Level [C] | 0.65 | -0.92 – 2.22 | 0.415 |
| Register [Fiction] * Level [D] | 1.25 | 0.23 – 2.27 | 0.016 |
| Register [Informative] * Level [D] | 0.69 | -0.32 – 1.70 | 0.183 |
| Register [Instructional] * Level [D] | -1.05 | -1.83 – -0.28 | 0.008 |
| Register [Personal] * Level [D] | 0.26 | -1.37 – 1.90 | 0.753 |
| Register [Fiction] * Level [E] | 0.97 | -0.09 – 2.02 | 0.072 |
| Register [Informative] * Level [E] | 0.84 | -0.22 – 1.91 | 0.120 |
| Register [Instructional] * Level [E] | -0.68 | -1.53 – 0.16 | 0.113 |
| Register [Personal] * Level [E] | 0.67 | -0.97 – 2.32 | 0.423 |
| Random Effects | |||
| σ2 | 4.40 | ||
| τ00 Series | 0.01 | ||
| ICC | 0.00 | ||
| N Series | 9 | ||
| Observations | 1912 | ||
| Marginal R2 / Conditional R2 | 0.472 / 0.474 | ||
# check distribution of residuals
library(predictmeans)## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapse## The following object is masked from 'package:lme4':
##
## lmList## Loading required package: parallelresidplot(md_final2)
# We can access the estimated deviation between each series average Dim2 and the overall average:
ranef(md_final2)## $Series
## (Intercept)
## Access -0.010235255
## Achievers -0.020008877
## EIM 0.060406291
## GreenLine -0.009780533
## HT -0.009027618
## JTT 0.007276723
## NGL -0.063482158
## POC -0.077522890
## Solutions 0.122374316
##
## with conditional variances for "Series"TxBdim_nopoetry[, "predicted"] <- predict(md_final2)
p <- ggplot(TxBdim_nopoetry, aes(x = Level, y = Dim2)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
facet_grid(rows = vars(Series), cols = vars(Register)) +
geom_point(aes(y = predicted), shape = "triangle filled", fill = "red")+
labs(y = "Dimension 2 (Biber 1988)", x = "Textbook Level")
top_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Register") +
theme_void()
right_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Series", angle = -90) +
theme_void()
blank_panel <- ggplot() +
theme_void()
grid.arrange(
top_label, blank_panel, p, right_label,
nrow = 2, ncol = 2,
widths = c(30, 1), heights = c(1, 30)
)
# Save plot using Rstudio export function (dimensions: 600 to 900 work well)
Register_results2 <- emmeans(md_final2, "Register")## NOTE: Results may be misleading due to involvement in interactionssummary(Register_results2)## Register emmean SE df lower.CL upper.CL
## Conversation -3.15 0.1045 67.8 -3.36 -2.94
## Fiction 2.34 0.1393 157.5 2.07 2.62
## Informative -1.84 0.1288 164.0 -2.09 -1.58
## Instructional -2.26 0.0941 51.5 -2.45 -2.08
## Personal -2.30 0.2329 1072.3 -2.76 -1.84
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95comparisons2 <- pairs(Register_results2, adjust = "tukey")
comparisons2## contrast estimate SE df t.ratio p.value
## Conversation - Fiction -5.4934 0.163 1475 -33.685 <.0001
## Conversation - Informative -1.3126 0.155 1717 -8.464 <.0001
## Conversation - Instructional -0.8846 0.128 1687 -6.923 <.0001
## Conversation - Personal -0.8477 0.248 1930 -3.414 0.0059
## Fiction - Informative 4.1808 0.182 1283 22.960 <.0001
## Fiction - Instructional 4.6088 0.157 1483 29.269 <.0001
## Fiction - Personal 4.6457 0.264 1917 17.582 <.0001
## Informative - Instructional 0.4280 0.147 1935 2.905 0.0304
## Informative - Personal 0.4650 0.259 1938 1.793 0.3779
## Instructional - Personal 0.0369 0.244 1935 0.151 0.9999
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 5 estimates# Visualising effects
visreg(md_final2, xvar = "Level", by="Register", type = "conditional", line=list(col="darkred"), xlab = "Textbook Level", ylab = "Dimension 2 (Biber 1988)")
# Group differences
emmeans(md_final2, ~ Level*Register)## Level Register emmean SE df lower.CL upper.CL
## A Conversation -4.085 0.223 870 -4.524 -3.6468
## B Conversation -3.819 0.198 680 -4.207 -3.4313
## C Conversation -3.188 0.198 633 -3.575 -2.7997
## D Conversation -2.342 0.202 626 -2.738 -1.9454
## E Conversation -2.310 0.250 982 -2.800 -1.8206
## A Fiction -0.136 0.357 1547 -0.836 0.5633
## B Fiction 2.921 0.291 1172 2.350 3.4914
## C Fiction 3.476 0.295 1341 2.898 4.0545
## D Fiction 2.858 0.259 1154 2.349 3.3656
## E Fiction 2.605 0.254 1116 2.107 3.1019
## A Informative -3.371 0.377 1722 -4.109 -2.6322
## B Informative -2.413 0.279 1335 -2.960 -1.8656
## C Informative -1.705 0.232 927 -2.160 -1.2493
## D Informative -0.940 0.217 893 -1.366 -0.5143
## E Informative -0.753 0.233 954 -1.209 -0.2961
## A Instructional -2.724 0.212 899 -3.141 -2.3066
## B Instructional -2.650 0.187 649 -3.017 -2.2833
## C Instructional -2.284 0.181 589 -2.638 -1.9293
## D Instructional -2.033 0.180 580 -2.386 -1.6795
## E Instructional -1.631 0.200 660 -2.024 -1.2380
## A Personal -3.513 0.587 1918 -4.665 -2.3611
## B Personal -3.457 0.453 1841 -4.345 -2.5694
## C Personal -1.963 0.474 1888 -2.893 -1.0338
## D Personal -1.506 0.530 1906 -2.545 -0.4675
## E Personal -1.066 0.514 1890 -2.075 -0.0575
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95Dim3
# Rainplots for all textbook registers on Dimension 3
TxBdimensions %>% group_by(Register) %>% summarise(mean = mean(Dim3), sd = sd(Dim3))## # A tibble: 6 × 3
## Register mean sd
## <fct> <dbl> <dbl>
## 1 Conversation -0.445 2.37
## 2 Fiction -0.649 2.74
## 3 Informative 3.12 3.07
## 4 Instructional 3.52 2.82
## 5 Personal -0.829 2.96
## 6 Poetry 0.919 3.77ggplot(TxBdimensions,aes(x=Register,y=Dim3, fill = Register, colour = Register))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
geom_boxplot(position = position_nudge(x = .25), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 3 (Biber 1988)')+xlab('Textbook Registers')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 3: Explicit vs. Situation-Dependent Reference") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 20, by = 5)) 
#ggsave(here("plots", "TxB_Reg_Dim3.svg"), width = 10, height = 6)
Dim3 <- TxBzscores %>%
select(Register, WH_rel_clauses_obj, Pied_piping_relative_clauses, WH_rel_clauses_subj, Phrasal_coord, Nominalizations, Time_adv, Place_adv, Adverbs) %>%
group_by(Register) %>%
summarise_all(mean) %>%
mutate_if(is.numeric, round, digits = 2) %>%
t()
Dim3## [,1] [,2] [,3]
## Register "Conversation" "Fiction" "Informative"
## WH_rel_clauses_obj "-0.75" "-0.76" "-0.63"
## Pied_piping_relative_clauses "-0.59" "-0.59" "-0.45"
## WH_rel_clauses_subj "-0.63" "-0.59" "-0.11"
## Phrasal_coord "0.60" "1.39" "2.40"
## Nominalizations "-0.91" "-0.95" "-0.30"
## Time_adv " 0.19" " 0.34" "-0.10"
## Place_adv "-0.11" " 0.77" " 0.24"
## Adverbs "-1.33" "-1.38" "-1.89"
## [,4] [,5] [,6]
## Register "Instructional" "Personal" "Poetry"
## WH_rel_clauses_obj "-0.58" "-0.80" "-0.80"
## Pied_piping_relative_clauses "-0.26" "-0.64" "-0.57"
## WH_rel_clauses_subj "-0.71" "-0.63" "-0.62"
## Phrasal_coord "2.31" "1.06" "2.81"
## Nominalizations " 0.35" "-0.85" "-1.11"
## Time_adv "-0.35" " 0.88" " 0.15"
## Place_adv " 0.39" " 0.10" " 0.62"
## Adverbs "-2.18" "-1.36" "-1.42"md_final3 <- lmer(Dim3 ~ (1|Series) + Register + Level + Level:Register, TxBdim_nopoetry, REML = FALSE) # no warnings
tab_model(md_final3, wrap.labels = 100) | Dim 3 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.18 | -1.13 – 0.77 | 0.715 |
| Register [Fiction] | -0.15 | -1.10 – 0.81 | 0.764 |
| Register [Informative] | 3.03 | 2.04 – 4.03 | <0.001 |
| Register [Instructional] | 5.16 | 4.46 – 5.85 | <0.001 |
| Register [Personal] | -0.04 | -1.47 – 1.39 | 0.957 |
| Level [B] | -0.29 | -0.96 – 0.37 | 0.389 |
| Level [C] | -0.24 | -0.91 – 0.42 | 0.471 |
| Level [D] | 0.52 | -0.15 – 1.19 | 0.128 |
| Level [E] | -0.08 | -0.84 – 0.67 | 0.829 |
| Register [Fiction] * Level [B] | 0.35 | -0.89 – 1.58 | 0.582 |
| Register [Informative] * Level [B] | 0.48 | -0.77 – 1.74 | 0.450 |
| Register [Instructional] * Level [B] | -0.89 | -1.81 – 0.03 | 0.058 |
| Register [Personal] * Level [B] | 0.18 | -1.64 – 2.00 | 0.844 |
| Register [Fiction] * Level [C] | -0.15 | -1.39 – 1.09 | 0.809 |
| Register [Informative] * Level [C] | 1.08 | -0.12 – 2.28 | 0.077 |
| Register [Instructional] * Level [C] | -1.52 | -2.43 – -0.61 | 0.001 |
| Register [Personal] * Level [C] | -0.56 | -2.41 – 1.28 | 0.550 |
| Register [Fiction] * Level [D] | -0.05 | -1.25 – 1.15 | 0.934 |
| Register [Informative] * Level [D] | -0.14 | -1.33 – 1.06 | 0.821 |
| Register [Instructional] * Level [D] | -2.13 | -3.04 – -1.21 | <0.001 |
| Register [Personal] * Level [D] | -2.10 | -4.03 – -0.17 | 0.033 |
| Register [Fiction] * Level [E] | -0.52 | -1.76 – 0.72 | 0.411 |
| Register [Informative] * Level [E] | 1.07 | -0.18 – 2.32 | 0.094 |
| Register [Instructional] * Level [E] | -1.08 | -2.07 – -0.08 | 0.033 |
| Register [Personal] * Level [E] | 0.18 | -1.75 – 2.12 | 0.852 |
| Random Effects | |||
| σ2 | 6.08 | ||
| τ00 Series | 1.51 | ||
| ICC | 0.20 | ||
| N Series | 9 | ||
| Observations | 1912 | ||
| Marginal R2 / Conditional R2 | 0.353 / 0.482 | ||
# check distribution of residuals
residplot(md_final3)
# We can access the estimated deviation between each series average Dim3 and the overall average:
ranef(md_final3)## $Series
## (Intercept)
## Access -0.6912930
## Achievers -0.6174253
## EIM -1.1919483
## GreenLine -0.8109561
## HT 1.9421314
## JTT 1.6915190
## NGL -0.2658934
## POC 1.3291986
## Solutions -1.3853329
##
## with conditional variances for "Series"TxBdim_nopoetry[, "predicted"] <- predict(md_final3)
p <- ggplot(TxBdim_nopoetry, aes(x = Level, y = Dim3)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
facet_grid(rows = vars(Series), cols = vars(Register)) +
geom_point(aes(y = predicted), shape = "triangle filled", fill = "red")+
labs(y = "Dimension 3 (Biber 1988)", x = "Textbook Level")
top_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Register") +
theme_void()
right_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Series", angle = -90) +
theme_void()
blank_panel <- ggplot() +
theme_void()
grid.arrange(
top_label, blank_panel, p, right_label,
nrow = 2, ncol = 2,
widths = c(30, 1), heights = c(1, 30)
)
# Save plot using Rstudio export function (dimensions: 600 to 900 work well)
Register_results3 <- emmeans(md_final3, "Register")## NOTE: Results may be misleading due to involvement in interactionssummary(Register_results3)## Register emmean SE df lower.CL upper.CL
## Conversation -0.197 0.450 11.3 -1.19 0.792
## Fiction -0.419 0.464 12.8 -1.42 0.585
## Informative 3.334 0.459 12.2 2.34 4.332
## Instructional 3.837 0.447 10.9 2.85 4.822
## Personal -0.696 0.513 19.8 -1.77 0.375
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95comparisons3 <- pairs(Register_results3, adjust = "tukey")
comparisons3## contrast estimate SE df t.ratio p.value
## Conversation - Fiction 0.222 0.194 1932 1.145 0.7824
## Conversation - Informative -3.531 0.183 1930 -19.250 <.0001
## Conversation - Instructional -4.034 0.151 1930 -26.689 <.0001
## Conversation - Personal 0.499 0.293 1929 1.703 0.4321
## Fiction - Informative -3.753 0.216 1932 -17.343 <.0001
## Fiction - Instructional -4.256 0.187 1932 -22.759 <.0001
## Fiction - Personal 0.277 0.312 1929 0.888 0.9015
## Informative - Instructional -0.503 0.173 1928 -2.898 0.0311
## Informative - Personal 4.030 0.305 1928 13.202 <.0001
## Instructional - Personal 4.533 0.287 1928 15.795 <.0001
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 5 estimates# Visualise effects
# Levels
visreg::visreg(md_final3, xvar = "Level", by="Register", type = "conditional", line=list(col="darkred"), xlab = "Textbook Level", ylab = "Dimension 3 (Biber 1988)")
# Textbook Series (random effect)
v <- visreg::visreg(md_final3, "Register", by="Series", re.form=~(1|Series), plot=FALSE)
plot(v, ylab="Dimension 3 (Biber 1988)", line=list(col="darkred"))
visreg(md_final3, xvar = "Series", by="Register", type = "conditional", re.form=~(1|Series), line=list(col="darkred"), xlab = "Textbook Series", ylab = "Dimension 3 (Biber 1988)", layout=c(1,5))
# Textual analysis
Dim3zscores <- TxBzscores %>%
select(Series, WH_rel_clauses_obj, WH_rel_clauses_subj, Pied_piping_relative_clauses, Phrasal_coord, Nominalizations, Place_adv, Time_adv, Adverbs) %>%
group_by(Series) %>%
summarise_all(mean) %>%
mutate_if(is.numeric, round, digits = 2) %>%
t()
Dim3zscores## [,1] [,2] [,3] [,4] [,5]
## Series "Access" "Achievers" "EIM" "GreenLine" "HT"
## WH_rel_clauses_obj "-0.75" "-0.63" "-0.62" "-0.72" "-0.65"
## WH_rel_clauses_subj "-0.67" "-0.72" "-0.66" "-0.61" "-0.40"
## Pied_piping_relative_clauses "-0.51" "-0.47" "-0.51" "-0.46" "-0.33"
## Phrasal_coord "1.57" "2.12" "1.25" "1.27" "2.40"
## Nominalizations "-0.76" " 0.10" "-0.49" "-0.70" " 0.01"
## Place_adv " 0.35" " 0.53" "-0.20" " 0.08" "-0.18"
## Time_adv " 0.16" " 0.14" " 0.32" " 0.12" "-0.25"
## Adverbs "-1.57" "-1.85" "-1.63" "-1.64" "-1.93"
## [,6] [,7] [,8] [,9]
## Series "JTT" "NGL" "POC" "Solutions"
## WH_rel_clauses_obj "-0.74" "-0.72" "-0.73" "-0.56"
## WH_rel_clauses_subj "-0.44" "-0.40" "-0.51" "-0.53"
## Pied_piping_relative_clauses "-0.39" "-0.50" "-0.50" "-0.42"
## Phrasal_coord "2.38" "1.39" "1.94" "1.37"
## Nominalizations "-0.25" "-0.56" "-0.24" "-0.31"
## Place_adv "-0.08" " 0.23" "-0.44" " 1.10"
## Time_adv "-0.41" "-0.01" "-0.36" " 0.03"
## Adverbs "-2.08" "-1.49" "-1.96" "-1.74"Dim4
ggplot(TxBdimensions,aes(x=Register,y=Dim4, fill = Register, colour = Register))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim4), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 4 (Biber 1988)')+xlab('Textbook Registers')+ theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 4: Overt Expression of Persuasion") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 25, by = 5))
#ggsave(here("plots", "TxB_Reg_Dim4.svg"), width = 10, height = 6)
TxBdimensions %>% filter(Dim4>20) # Outlier## Filename Level Dim1 Dim2 Dim3 Dim4 Dim5
## 1 JTT_5_Informative_0007.txt B 21.3275 -0.9109 -1.5774 25.5345 1.0514
## Dim6 TextType Register Series Country
## 1 -1.1141 Informational interaction Informative JTT FranceTxBdimensions %>% group_by(Register) %>% summarise(mean = mean(Dim4))## # A tibble: 6 × 2
## Register mean
## <fct> <dbl>
## 1 Conversation -1.33
## 2 Fiction -1.69
## 3 Informative -1.72
## 4 Instructional -2.10
## 5 Personal -0.366
## 6 Poetry -0.340TxBdimensions2 <- TxBdimensions %>% filter(Filename!="JTT_5_Informative_0007.txt") # Remove outlier for testing
ggplot(TxBdimensions2,aes(x=Register,y=Dim4, fill = Register, colour = Register))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim4), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 4 (Biber 1988)')+xlab('Textbook Registers')+ theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 4: Overt Expression of Persuasion") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 25, by = 5))
#ggsave(here("plots", "TxB_Reg_Dim4.svg"), width = 10, height = 6)TxBdim2_nopoetry <- TxBdimensions2 %>% filter(Register != "Poetry") %>% droplevels(.)
summary(TxBdim_nopoetry$Register) # Check## Conversation Fiction Informative Instructional Personal
## 529 285 363 647 88md_final4 <- lmer(Dim4 ~ (1|Series) + Register + Level + Level:Register, TxBdim2_nopoetry, REML = FALSE) # no warnings
tab_model(md_final4, wrap.labels = 100) | Dim 4 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -5.74 | -6.56 – -4.91 | <0.001 |
| Register [Fiction] | 1.01 | -0.23 – 2.24 | 0.110 |
| Register [Informative] | 0.98 | -0.30 – 2.27 | 0.134 |
| Register [Instructional] | 1.75 | 0.85 – 2.64 | <0.001 |
| Register [Personal] | 0.49 | -1.37 – 2.34 | 0.608 |
| Level [B] | 3.83 | 2.97 – 4.70 | <0.001 |
| Level [C] | 5.96 | 5.11 – 6.82 | <0.001 |
| Level [D] | 5.69 | 4.82 – 6.56 | <0.001 |
| Level [E] | 6.38 | 5.39 – 7.36 | <0.001 |
| Register [Fiction] * Level [B] | -1.59 | -3.19 – 0.00 | 0.050 |
| Register [Informative] * Level [B] | -2.43 | -4.06 – -0.80 | 0.003 |
| Register [Instructional] * Level [B] | -2.49 | -3.67 – -1.30 | <0.001 |
| Register [Personal] * Level [B] | -0.11 | -2.46 – 2.24 | 0.928 |
| Register [Fiction] * Level [C] | -1.82 | -3.42 – -0.22 | 0.026 |
| Register [Informative] * Level [C] | -2.89 | -4.44 – -1.33 | <0.001 |
| Register [Instructional] * Level [C] | -3.94 | -5.12 – -2.76 | <0.001 |
| Register [Personal] * Level [C] | -0.32 | -2.71 – 2.07 | 0.793 |
| Register [Fiction] * Level [D] | -1.21 | -2.76 – 0.35 | 0.128 |
| Register [Informative] * Level [D] | -2.13 | -3.67 – -0.59 | 0.007 |
| Register [Instructional] * Level [D] | -3.59 | -4.77 – -2.40 | <0.001 |
| Register [Personal] * Level [D] | 2.76 | 0.27 – 5.26 | 0.030 |
| Register [Fiction] * Level [E] | -2.20 | -3.81 – -0.59 | 0.007 |
| Register [Informative] * Level [E] | -2.39 | -4.02 – -0.77 | 0.004 |
| Register [Instructional] * Level [E] | -2.85 | -4.14 – -1.56 | <0.001 |
| Register [Personal] * Level [E] | -0.12 | -2.63 – 2.38 | 0.923 |
| Random Effects | |||
| σ2 | 10.18 | ||
| τ00 Series | 0.59 | ||
| ICC | 0.06 | ||
| N Series | 9 | ||
| Observations | 1911 | ||
| Marginal R2 / Conditional R2 | 0.213 / 0.256 | ||
# check distribution of residuals
library(predictmeans)
residplot(md_final4)
# We can access the estimated deviation between each series average Dim4 and the overall average:
ranef(md_final4)## $Series
## (Intercept)
## Access -0.51443745
## Achievers 0.51979262
## EIM 0.02697037
## GreenLine -0.62892523
## HT -1.29336978
## JTT -0.06114683
## NGL -0.11547528
## POC 1.29938009
## Solutions 0.76721149
##
## with conditional variances for "Series"TxBdim2_nopoetry[, "predicted"] <- predict(md_final4)
p <- ggplot(TxBdim2_nopoetry, aes(x = Level, y = Dim4)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
facet_grid(rows = vars(Series), cols = vars(Register)) +
geom_point(aes(y = predicted), shape = "triangle filled", fill = "red")+
labs(y = "Dimension 4 (Biber 1988)", x = "Textbook Level")
top_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Register") +
theme_void()
right_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Series", angle = -90) +
theme_void()
blank_panel <- ggplot() +
theme_void()
grid.arrange(
top_label, blank_panel, p, right_label,
nrow = 2, ncol = 2,
widths = c(30, 1), heights = c(1, 30)
)
# Save plot using Rstudio export function (dimensions: 600 to 900 work well)
Register_results4 <- emmeans(md_final4, "Register")## NOTE: Results may be misleading due to involvement in interactionssummary(Register_results4)## Register emmean SE df lower.CL upper.CL
## Conversation -1.366 0.312 15.1 -2.03 -0.70
## Fiction -1.724 0.344 22.8 -2.44 -1.01
## Informative -2.350 0.333 20.0 -3.04 -1.66
## Instructional -2.192 0.304 13.6 -2.85 -1.54
## Personal -0.439 0.445 68.1 -1.33 0.45
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95comparisons4 <- pairs(Register_results4, adjust = "tukey")
comparisons4## contrast estimate SE df t.ratio p.value
## Conversation - Fiction 0.358 0.251 1936 1.430 0.6085
## Conversation - Informative 0.985 0.238 1935 4.146 0.0003
## Conversation - Instructional 0.827 0.196 1935 4.228 0.0002
## Conversation - Personal -0.927 0.379 1931 -2.447 0.1035
## Fiction - Informative 0.626 0.280 1936 2.237 0.1666
## Fiction - Instructional 0.468 0.242 1936 1.936 0.2983
## Fiction - Personal -1.285 0.403 1931 -3.187 0.0127
## Informative - Instructional -0.158 0.225 1929 -0.703 0.9558
## Informative - Personal -1.912 0.395 1928 -4.838 <.0001
## Instructional - Personal -1.754 0.371 1928 -4.722 <.0001
##
## Results are averaged over the levels of: Level
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 5 estimates# Visualise effects
visreg(md_final4, xvar = "Level", by="Register", type = "conditional", line=list(col="darkred"), xlab = "Textbook Level", ylab = "Dimension 4 (Biber 1988)")
# Or using a ggplot2 approach
ggplot(TxBdim_nopoetry, aes(x = Level, y = Dim4, colour = Register)) +
geom_point(position = position_jitter(width = 0.27), size = 2, alpha = 0.8)+
geom_boxplot(aes(x = Level, y = Dim4),
outlier.shape = NA, alpha = 0.3, width = .6, colour = "BLACK") +
scale_colour_manual(values = colours[c(1,2,6,4,5)])+
scale_fill_manual(values = colours[c(1,2,6,4,5)])+
scale_y_continuous(limits=c(-10,15))+ # Removes two outliers
theme_minimal() +
theme(legend.position = "bottom", legend.title.align = 0.5, legend.margin=margin(0,0,0,0))+
ylab('Dimension 4 (Biber 1988)')## Warning: Removed 2 rows containing non-finite values (stat_boxplot).## Warning: Removed 2 rows containing missing values (geom_point).
#ggsave(here("plots", "Dim4_boxplot_levels.svg"), width = 7, height = 9)
# Z-scores
dim4zscores <- TxBzscores %>%
select(Filename, Level, To_infinitives, Pred_modals, Suasive_verbs, if_unless, Necessity_modals, Split_auxiliaries) %>%
group_by(Level) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
print.data.frame## Level To_infinitives Pred_modals Suasive_verbs if_unless Necessity_modals
## 1 A -0.93985088 -0.78691123 0.1541263 -0.77716351 -0.4392207
## 2 B -0.06722843 -0.22506983 0.2294970 -0.34661546 -0.1909284
## 3 C 0.26932696 0.14349401 0.2810493 0.16012488 -0.2997090
## 4 D 0.35551982 0.08949912 0.2987740 0.09240969 -0.3655297
## 5 E 0.62622880 0.05079893 0.2649056 0.08877387 -0.3043565
## Split_auxiliaries
## 1 -1.959834
## 2 -1.722025
## 3 -1.450334
## 4 -1.233483
## 5 -1.249927Dim5
# Rainplots for all textbook registers on Dimension 5
ggplot(TxBdimensions,aes(x=Register,y=Dim5, fill = Register, colour = Register))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
geom_boxplot(position = position_nudge(x = .25), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 5 (Biber 1988)')+xlab('Textbook Registers')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 5: Abstract vs. Non-Abstract Information ")+
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -4, to = 10, by = 2))
#ggsave(here("plots", "TxB_Reg_Dim5.svg"), width = 10, height = 6)
# Mean Dim5 scores
TxBdimensions %>% group_by(Register) %>% summarise(mean = mean(Dim5), sd = sd(Dim5))## # A tibble: 6 × 3
## Register mean sd
## <fct> <dbl> <dbl>
## 1 Conversation -2.20 1.75
## 2 Fiction -1.91 1.76
## 3 Informative -0.108 2.43
## 4 Instructional -2.75 1.29
## 5 Personal -1.98 1.86
## 6 Poetry -2.67 1.46# Lots of texts reach the floor level:
TxBdimensions %>% filter(Dim5 < -3.915) %>% summarise(n = n())## n
## 1 265# Clear level effect for Informative texts:
TxBdimensions %>% filter(Register=="Informative") %>% group_by(Level) %>% summarise(mean = mean(Dim5), sd = sd(Dim5))## # A tibble: 5 × 3
## Level mean sd
## <fct> <dbl> <dbl>
## 1 A -2.52 0.989
## 2 B -1.18 1.94
## 3 C 0.0835 2.39
## 4 D 0.459 2.28
## 5 E 0.680 2.54# Floor effect might be due to low frequencies of the features on Dimension 5 in the beginner/pre-intermediate textbook:
TxBdimensions %>% filter(as.numeric(Level)>3) %>% group_by(Register) %>% summarise(mean = mean(Dim5), sd = sd(Dim5))## # A tibble: 6 × 3
## Register mean sd
## <fct> <dbl> <dbl>
## 1 Conversation -1.42 2.02
## 2 Fiction -1.36 1.63
## 3 Informative 0.562 2.40
## 4 Instructional -2.39 1.32
## 5 Personal -1.50 1.58
## 6 Poetry -2.25 1.54TxBdimensionsLevelsDE <- TxBdimensions %>% filter(as.numeric(Level)>3)
ggplot(TxBdimensionsLevelsDE,aes(x=Register,y=Dim5, fill = Register, colour = Register))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim5), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 5 (Biber 1988)')+xlab('Textbook Registers (Levels D and E only)')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 5: Abstract vs. Non-Abstract Information") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -4, to = 10, by = 2))
#ggsave(here("plots", "TxB_Reg_Dim5_LevelsD-E.svg"), width = 10, height = 6)md_final5 <- lmer(Dim5 ~ (1|Series) + Register + Level + Level:Register, TxBdim_nopoetry, REML = FALSE) # no warnings
tab_model(md_final5, wrap.labels = 100)| Dim 5 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -3.48 | -3.87 – -3.09 | <0.001 |
| Register [Fiction] | 0.12 | -0.49 – 0.72 | 0.709 |
| Register [Informative] | 0.78 | 0.14 – 1.41 | 0.016 |
| Register [Instructional] | -0.21 | -0.65 – 0.23 | 0.354 |
| Register [Personal] | -0.17 | -1.08 – 0.75 | 0.717 |
| Level [B] | 0.48 | 0.06 – 0.91 | 0.027 |
| Level [C] | 1.53 | 1.10 – 1.95 | <0.001 |
| Level [D] | 2.06 | 1.63 – 2.48 | <0.001 |
| Level [E] | 1.85 | 1.37 – 2.33 | <0.001 |
| Register [Fiction] * Level [B] | 0.56 | -0.22 – 1.35 | 0.159 |
| Register [Informative] * Level [B] | 0.93 | 0.13 – 1.72 | 0.023 |
| Register [Instructional] * Level [B] | 0.21 | -0.37 – 0.80 | 0.480 |
| Register [Personal] * Level [B] | 0.67 | -0.49 – 1.83 | 0.256 |
| Register [Fiction] * Level [C] | 0.26 | -0.53 – 1.05 | 0.514 |
| Register [Informative] * Level [C] | 1.07 | 0.30 – 1.83 | 0.006 |
| Register [Instructional] * Level [C] | -0.56 | -1.14 – 0.03 | 0.061 |
| Register [Personal] * Level [C] | 1.06 | -0.12 – 2.24 | 0.078 |
| Register [Fiction] * Level [D] | 0.38 | -0.39 – 1.14 | 0.333 |
| Register [Informative] * Level [D] | 1.02 | 0.25 – 1.78 | 0.009 |
| Register [Instructional] * Level [D] | -0.91 | -1.49 – -0.33 | 0.002 |
| Register [Personal] * Level [D] | 0.07 | -1.16 – 1.30 | 0.915 |
| Register [Fiction] * Level [E] | 0.03 | -0.77 – 0.82 | 0.949 |
| Register [Informative] * Level [E] | 1.66 | 0.86 – 2.46 | <0.001 |
| Register [Instructional] * Level [E] | -0.29 | -0.92 – 0.35 | 0.378 |
| Register [Personal] * Level [E] | 0.53 | -0.71 – 1.76 | 0.403 |
| Random Effects | |||
| σ2 | 2.47 | ||
| τ00 Series | 0.12 | ||
| ICC | 0.05 | ||
| N Series | 9 | ||
| Observations | 1912 | ||
| Marginal R2 / Conditional R2 | 0.358 / 0.387 | ||
# check distribution of residuals
residplot(md_final5) # Major issues!!
TxBdim_nopoetry %>% filter(Dim5==min(TxBdim_nopoetry$Dim5)) %>% nrow(.) # There are 257/1949 texts with the minimum Dim5 score. We obviously have a floor effect here.## [1] 257### Only looking at the upper level textbooks and excluding instructional language
UpperTxBDim <- TxBdim_nopoetry %>% filter(Level %in% c("C", "D", "E") & Register != "Instructional") %>% droplevels(.)
UpperTxBDim %>% filter(Dim5==-3.9201) # Reduces number of texts with the floor level to just 16## Filename Level Dim1 Dim2 Dim3
## 1 Access_3_Narrative_0013.txt C -2.4361 3.8357 -8.2436
## 2 Access_3_Spoken_0001.txt C 12.8537 -5.6998 -0.7944
## 3 Access_3_Spoken_0013.txt C 5.9886 -3.1583 -0.0380
## 4 Access_5_Personal_0001.txt E 8.1586 -4.2561 0.6477
## 5 EIM_2_Informative_0003.txt C 2.9309 -0.2069 0.2607
## 6 EIM_2_Personal_0001.txt C 14.2851 -1.6148 -3.9124
## 7 EIM_2_Spoken_0005.txt C 25.1282 -4.8196 -3.2386
## 8 GreenLine_3_Narrative_0011.txt C 0.3915 5.1584 -3.6444
## 9 GreenLine_3_Spoken_0003.txt C 23.3761 -3.3375 0.5133
## 10 GreenLine_3_Spoken_0007.txt C 16.2062 -3.7157 -1.8924
## 11 JTT_3_Spoken_0002.txt D 31.1243 -2.5753 -1.1061
## 12 NGL_3_Narrative_0005.txt C 5.1243 6.3279 1.3942
## 13 NGL_4_Spoken_0004.txt D 13.0234 -3.3312 1.0554
## 14 NGL_4_Spoken_0010.txt D 18.7797 -2.9715 -2.0073
## 15 POC_3e_Spoken_0016.txt D 24.9732 -4.9363 -1.6696
## 16 Solutions_Intermediate_Informative_0018.txt C 4.4176 -6.4075 -2.3155
## Dim4 Dim5 Dim6 TextType Register Series
## 1 -3.2271 -3.9201 -2.4462 Situated reportage Fiction Access
## 2 -2.5996 -3.9201 -0.5986 Involved persuasion Conversation Access
## 3 -3.8504 -3.9201 -2.5627 Involved persuasion Conversation Access
## 4 -5.1964 -3.9201 -2.7798 Involved persuasion Personal Access
## 5 2.1652 -3.9201 1.5124 Involved persuasion Informative EIM
## 6 0.4186 -3.9201 -2.5070 Involved persuasion Personal EIM
## 7 5.6688 -3.9201 -2.3547 Informational interaction Conversation EIM
## 8 -5.9071 -3.9201 -1.8820 Imaginative narrative Fiction GreenLine
## 9 0.2310 -3.9201 -1.9659 Informational interaction Conversation GreenLine
## 10 -4.0050 -3.9201 2.2155 Involved persuasion Conversation GreenLine
## 11 -1.0901 -3.9201 -1.9952 Informational interaction Conversation JTT
## 12 1.8832 -3.9201 -1.3817 Imaginative narrative Fiction NGL
## 13 -3.2447 -3.9201 -0.1897 Involved persuasion Conversation NGL
## 14 -5.1905 -3.9201 -1.3047 Informational interaction Conversation NGL
## 15 -1.4393 -3.9201 -1.8179 Informational interaction Conversation POC
## 16 1.4029 -3.9201 -0.0561 Involved persuasion Informative Solutions
## Country predicted
## 1 Germany -1.41151036
## 2 Germany -1.11244256
## 3 Germany -1.11244256
## 4 Germany -0.80723990
## 5 Spain 2.50229258
## 6 Spain -2.21681993
## 7 Spain -1.61309777
## 8 Germany -1.53117343
## 9 Germany -1.23210563
## 10 Germany -1.23210563
## 11 France 2.03578654
## 12 Germany -0.98611074
## 13 Germany 0.07837407
## 14 Germany 0.07837407
## 15 France 1.67346609
## 16 Spain 2.30890799md_final5_Upper <- lmer(Dim5 ~ (1|Series) + Register + Level + Level:Register, UpperTxBDim, REML = FALSE) # no warnings
tab_model(md_final5_Upper, wrap.labels = 100) # Hardly explains anything and is therefore really not worth the trouble| Dim 5 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -2.07 | -2.58 – -1.55 | <0.001 |
| Register [Fiction] | 0.57 | -0.05 – 1.20 | 0.071 |
| Register [Informative] | 1.78 | 1.25 – 2.31 | <0.001 |
| Register [Personal] | 1.02 | 0.12 – 1.92 | 0.026 |
| Level [D] | 0.51 | 0.03 – 0.99 | 0.036 |
| Level [E] | 0.48 | -0.09 – 1.04 | 0.096 |
| Register [Fiction] * Level [D] | 0.13 | -0.70 – 0.97 | 0.752 |
| Register [Informative] * Level [D] | 0.05 | -0.68 – 0.78 | 0.886 |
| Register [Personal] * Level [D] | -0.96 | -2.29 – 0.38 | 0.160 |
| Register [Fiction] * Level [E] | -0.39 | -1.26 – 0.48 | 0.375 |
| Register [Informative] * Level [E] | 0.67 | -0.12 – 1.46 | 0.095 |
| Register [Personal] * Level [E] | -0.62 | -1.97 – 0.72 | 0.363 |
| Random Effects | |||
| σ2 | 3.57 | ||
| τ00 Series | 0.34 | ||
| ICC | 0.09 | ||
| N Series | 9 | ||
| Observations | 833 | ||
| Marginal R2 / Conditional R2 | 0.184 / 0.255 | ||
# check distribution of residuals
library(predictmeans)
residplot(md_final5_Upper)
###
UpperTxBDim[, "predicted"] <- predict(md_final5_Upper)
p <- ggplot(UpperTxBDim, aes(x = Level, y = Dim5)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
facet_grid(rows = vars(Series), cols = vars(Register)) +
geom_point(aes(y = predicted), shape = "triangle filled", fill = "red")+
labs(y = "Dimension 5 (Biber 1988)", x = "Textbook Level")
top_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Register") +
theme_void()
right_label <- ggplot() +
annotate("text", x = 0, y = 0, label = "Textbook Series", angle = -90) +
theme_void()
blank_panel <- ggplot() +
theme_void()
grid.arrange(
top_label, blank_panel, p, right_label,
nrow = 2, ncol = 2,
widths = c(30, 1), heights = c(1, 30)
)Dim6
# Rainplots for all textbook registers on Dimension 6
ggplot(TxBdimensions,aes(x=Register,y=Dim6, fill = Register, colour = Register))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim6), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 6 (Biber 1988)')+xlab('Textbook Registers')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
#ggtitle("Dimension 6: On-Line Informational Elaboration") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -4, to = 10, by = 2))
#ggsave(here("plots", "TxB_Reg_Dim6.svg"), width = 10, height = 6)
TxBdimensions %>% filter(Dim6 > 5) # Two clear outliers## Filename Level Dim1 Dim2 Dim3 Dim4
## 1 Access_2_Spoken_0016.txt B 7.5089 -1.3987 -1.1996 -1.9249
## 2 Solutions_Intermediate_Poetry_0001.txt C 24.7457 -4.8622 1.4280 7.8919
## Dim5 Dim6 TextType Register Series Country
## 1 -3.5864 5.2576 Involved persuasion Conversation Access Germany
## 2 -3.1268 6.7785 Informational interaction Poetry Solutions SpainTxBdimensions %>% group_by(Register) %>% summarise(mean = mean(Dim6))## # A tibble: 6 × 2
## Register mean
## <fct> <dbl>
## 1 Conversation -1.48
## 2 Fiction -1.68
## 3 Informative -1.45
## 4 Instructional -1.75
## 5 Personal -1.76
## 6 Poetry -1.58summary(TxBdimensions$Dim6)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.495 -2.451 -1.781 -1.609 -0.996 6.779# Floor level:
TxBdimensions %>% filter(Dim6 < -3.494) %>% summarise(n = n()) # 66## n
## 1 66md_final6 <- lmer(Dim6 ~ (1|Series) + Register + Level + Level:Register, TxBdim_nopoetry, REML = FALSE) # no warnings
tab_model(md_final6, wrap.labels = 100) | Dim 6 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -2.38 | -2.63 – -2.13 | <0.001 |
| Register [Fiction] | 0.21 | -0.21 – 0.63 | 0.330 |
| Register [Informative] | 0.16 | -0.28 – 0.60 | 0.468 |
| Register [Instructional] | 0.16 | -0.15 – 0.47 | 0.306 |
| Register [Personal] | -0.24 | -0.88 – 0.40 | 0.459 |
| Level [B] | 0.64 | 0.35 – 0.94 | <0.001 |
| Level [C] | 0.78 | 0.48 – 1.07 | <0.001 |
| Level [D] | 1.31 | 1.01 – 1.61 | <0.001 |
| Level [E] | 1.61 | 1.27 – 1.94 | <0.001 |
| Register [Fiction] * Level [B] | -0.45 | -0.99 – 0.10 | 0.109 |
| Register [Informative] * Level [B] | -0.22 | -0.78 – 0.34 | 0.438 |
| Register [Instructional] * Level [B] | -0.24 | -0.65 – 0.17 | 0.244 |
| Register [Personal] * Level [B] | 0.01 | -0.80 – 0.82 | 0.979 |
| Register [Fiction] * Level [C] | -0.02 | -0.57 – 0.53 | 0.930 |
| Register [Informative] * Level [C] | 0.09 | -0.44 – 0.62 | 0.739 |
| Register [Instructional] * Level [C] | -0.24 | -0.64 – 0.17 | 0.253 |
| Register [Personal] * Level [C] | 0.10 | -0.72 – 0.93 | 0.802 |
| Register [Fiction] * Level [D] | -0.69 | -1.22 – -0.16 | 0.011 |
| Register [Informative] * Level [D] | -0.28 | -0.81 – 0.25 | 0.300 |
| Register [Instructional] * Level [D] | -0.81 | -1.22 – -0.40 | <0.001 |
| Register [Personal] * Level [D] | -0.28 | -1.14 – 0.57 | 0.515 |
| Register [Fiction] * Level [E] | -0.97 | -1.52 – -0.41 | 0.001 |
| Register [Informative] * Level [E] | -0.79 | -1.34 – -0.23 | 0.006 |
| Register [Instructional] * Level [E] | -0.87 | -1.31 – -0.42 | <0.001 |
| Register [Personal] * Level [E] | 0.03 | -0.83 – 0.89 | 0.946 |
| Random Effects | |||
| σ2 | 1.20 | ||
| τ00 Series | 0.03 | ||
| ICC | 0.02 | ||
| N Series | 9 | ||
| Observations | 1912 | ||
| Marginal R2 / Conditional R2 | 0.106 / 0.127 | ||
# check distribution of residuals
residplot(md_final6) # Not looking terribly good here, either!
ggplot(TxBdim_nopoetry, aes(x = Level, y = Dim6, facet = Register)) +
geom_point(position = position_jitter(width = .15), size = .25, alpha = 0.7)+
geom_boxplot(aes(x = Level, y = Dim6, fill = Register),
outlier.shape = NA, alpha = 0.9, width = .15) +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
scale_y_continuous(limits=c(-3.5,4))+
facet_grid(cols = vars(Register)) +
theme_minimal()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).## Warning: Removed 1 rows containing missing values (geom_point).ggplot(TxBdim_nopoetry, aes(x = Level, y = Dim6, colour = Register)) +
geom_point(position = position_jitter(width = 0.3), size = 2, alpha = 0.8)+
geom_boxplot(aes(x = Level, y = Dim6),
outlier.shape = NA, alpha = 0.3, width = .6, colour = "BLACK") +
scale_colour_manual(values = colours[c(1,2,6,4,5)])+
scale_fill_manual(values = colours[c(1,2,6,4,5)])+
scale_y_continuous(limits=c(-3.5,3.9), breaks = c(-3,-2,-1,0,1,2,3,4))+ # Removes the outlier in Conversation (discussed in Diss. chapter)
theme_minimal() +
theme(legend.position = "bottom", legend.title.align = 0.5, legend.margin=margin(0,0,0,0))+
ylab('Dimension 6 (Biber 1988)')## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
## Warning: Removed 1 rows containing missing values (geom_point).#ggsave(here("plots", "Dim6_boxplot_levels.svg"), width = 7, height = 9)
visreg(md_final6, xvar = "Level", by="Register", type = "conditional", line=list(col="darkred"), xlab = "Textbook Level", ylab = "Biber's Dimension 6")
# Model with upper levels only
TxBdim_nopoetry %>% filter(Dim6==min(TxBdim_nopoetry$Dim6)) %>% nrow(.) # There are 60/1949 texts with the minimum Dim6 score. We obviously have a floor effect here.## [1] 60minDim6 <- min(TxBdim_nopoetry[, "Dim6"])
UpperTxBDim <- TxBdim_nopoetry %>% filter(Level %in% c("C", "D", "E") & Register != "Instructional") %>% droplevels(.)
UpperTxBDim %>% filter(Dim6==min(TxBdim_nopoetry$Dim6)) %>% nrow(.) # Now only 20/1949## [1] 20md_final6_Upper <- update(md_final6, data = UpperTxBDim)
tab_model(md_final6_Upper, wrap.labels = 100) | Dim 6 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.67 | -1.98 – -1.36 | <0.001 |
| Register [Fiction] | 0.39 | -0.01 – 0.78 | 0.055 |
| Register [Informative] | 0.17 | -0.16 – 0.51 | 0.308 |
| Register [Personal] | -0.05 | -0.62 – 0.53 | 0.876 |
| Level [D] | 0.52 | 0.21 – 0.82 | 0.001 |
| Level [E] | 0.94 | 0.58 – 1.29 | <0.001 |
| Register [Fiction] * Level [D] | -0.67 | -1.20 – -0.14 | 0.013 |
| Register [Informative] * Level [D] | -0.27 | -0.74 – 0.19 | 0.248 |
| Register [Personal] * Level [D] | -0.35 | -1.20 – 0.50 | 0.416 |
| Register [Fiction] * Level [E] | -1.10 | -1.65 – -0.54 | <0.001 |
| Register [Informative] * Level [E] | -0.82 | -1.32 – -0.32 | 0.001 |
| Register [Personal] * Level [E] | -0.14 | -0.99 – 0.71 | 0.747 |
| Random Effects | |||
| σ2 | 1.44 | ||
| τ00 Series | 0.11 | ||
| ICC | 0.07 | ||
| N Series | 9 | ||
| Observations | 833 | ||
| Marginal R2 / Conditional R2 | 0.042 / 0.112 | ||
residplot(md_final6_Upper) # Not ideal but better
visreg::visreg(md_final6_Upper, xvar = "Level", by="Register", type = "conditional", line=list(col="darkred"), xlab = "Textbook Level", ylab = "Dimension 6 (Biber 1988)")Comparative Additive MDA
Data import
Reference corpora
# Spoken BNC2014 no mark-up (Jack & Jill version)
# Z-scores of individual features
BNCzscores <- read.delim(here("data", "SpokenBNC2014full_zscores.tsv"), header = TRUE)
BNCzscores <- BNCzscores %>% filter(Filename!=".DS_Store" & Filename!="CORPUS") # Remove this rows
colnames(BNCzscores)## [1] "Filename" "AMP" "ANDC" "AWL" "CAUS" "CONC"
## [7] "COND" "CONJ" "DEMO" "DEMP" "DPAR" "DWNT"
## [13] "EMPH" "EX" "FPP1" "GER" "HDG" "INPR"
## [19] "JJ" "NEMD" "NN" "NOMZ" "OSUB" "PHC"
## [25] "PIN" "PIT" "PLACE" "POMD" "PRED" "PRMD"
## [31] "RB" "SPP2" "SYNE" "THAC" "THVC" "TIME"
## [37] "TO" "TOBJ" "TPP3" "TSUB" "TTR" "VBD"
## [43] "VPRT" "XX0" "X.BEMA." "X.BYPA." "X.CONT." "X.PASS."
## [49] "X.PASTP." "X.PEAS." "X.PIRE." "X.PRESP." "X.PRIV." "X.PROD."
## [55] "X.PUBV." "X.SERE." "X.SMP." "X.SPAU." "X.SPIN." "X.STPR."
## [61] "X.SUAV." "X.THATD." "X.WHCL." "X.WHOBJ." "X.WHQU." "X.WHSUB."
## [67] "X.WZPAST." "X.WZPRES." "Underused" "Overused"MAT_features <- read.csv(here("metadata", "MAT_features_zscores_ref.csv"), header = F)
MAT_features <- MAT_features$V3 # Feature names without any spaces or hythens
colnames(BNCzscores) <- MAT_features
colnames(BNCzscores)## [1] "Filename" "Amplifiers"
## [3] "Ind_clause_coord" "AWL"
## [5] "because" "Coord_conj"
## [7] "if_unless" "although_though"
## [9] "Demonstratives" "Dem_pronouns"
## [11] "Discourse_particles" "Downtoners"
## [13] "Emphatics" "Existential_there"
## [15] "FPP1" "Gerunds"
## [17] "Hedges" "Ind_pronouns"
## [19] "Attr_Adj" "Necessity_modals"
## [21] "Nouns" "Nominalizations"
## [23] "Adv_sub" "Phrasal_coord"
## [25] "Prepositions" "it"
## [27] "Place_adv" "Poss_modals"
## [29] "Pred_adj" "Pred_modals"
## [31] "Adverbs" "SPP2"
## [33] "Syn_negation" "That_adj_comp"
## [35] "That_verb_comp" "Time_adv"
## [37] "To_infinitives" "That_rel_clause_obj"
## [39] "TPP3" "That_rel_clause_subj"
## [41] "TTR" "Past_tense"
## [43] "Present_tense" "Analytic_negation"
## [45] "BE_main_verb" "By_passives"
## [47] "Contractions" "Agentless_passives"
## [49] "Past_participial_clauses" "Perfect_aspect"
## [51] "Pied_piping_relative_clauses" "Present_part_clauses"
## [53] "Private_verbs" "Pro_verb_DO"
## [55] "Public_verbs" "Sentence_relatives"
## [57] "seem_appear" "Split_auxiliaries"
## [59] "Split_infinitives" "Stranded_prepositions"
## [61] "Suasive_verbs" "That_deletion"
## [63] "WH_clauses" "WH_rel_clauses_obj"
## [65] "Direct_WH_questions" "WH_rel_clauses_subj"
## [67] "Past_part_WHIZ_del_rel" "Present_part_WHIZ_del_rel"
## [69] "Underused" "Overused"# Dimensions Biber (1988)
BNCdimensions <- read.delim(here("data", "SpokenBNC2014full_dims.tsv"), header = TRUE)
#View(BNCdimensions)
glimpse(BNCdimensions)## Rows: 1,253
## Columns: 8
## $ Filename <chr> "SFYX.txt", "SVH7.txt", "S75W.txt", "SH63.txt", "SAZ8.txt", "…
## $ Dim1 <dbl> 22.1993, 23.4792, 33.6168, 30.9853, 23.9637, 28.6082, 20.9903…
## $ Dim2 <dbl> -1.9363, -2.9455, -1.6510, -0.2400, -3.1933, -0.6629, -3.1299…
## $ Dim3 <dbl> -0.7729, -0.8446, -2.5404, -3.0247, 0.5859, -3.1513, 0.4093, …
## $ Dim4 <dbl> -2.0041, 0.3389, -0.1011, -1.9232, 4.4512, -0.2231, 0.5990, -…
## $ Dim5 <dbl> -0.8758, -1.0732, -2.5485, -3.2441, -1.9479, -1.4352, -2.2731…
## $ Dim6 <dbl> -0.6297, 0.4310, -1.0667, -0.4676, -0.2071, -0.3209, 2.0316, …
## $ TextType <chr> "Informational interaction", "Informational interaction", "In…head(BNCdimensions)## Filename Dim1 Dim2 Dim3 Dim4 Dim5 Dim6
## 1 SFYX.txt 22.1993 -1.9363 -0.7729 -2.0041 -0.8758 -0.6297
## 2 SVH7.txt 23.4792 -2.9455 -0.8446 0.3389 -1.0732 0.4310
## 3 S75W.txt 33.6168 -1.6510 -2.5404 -0.1011 -2.5485 -1.0667
## 4 SH63.txt 30.9853 -0.2400 -3.0247 -1.9232 -3.2441 -0.4676
## 5 SAZ8.txt 23.9637 -3.1933 0.5859 4.4512 -1.9479 -0.2071
## 6 SPG4.txt 28.6082 -0.6629 -3.1513 -0.2231 -1.4352 -0.3209
## TextType
## 1 Informational interaction
## 2 Informational interaction
## 3 Informational interaction
## 4 Informational interaction
## 5 Informational interaction
## 6 Informational interactionBNCdimensions <- BNCdimensions %>% filter(Filename!="CORPUS") %>% filter(Filename!=".DS_Store") # Remove mean values output by the MAT + .DS_Store file due to working with the MAT on a MAC
# Adding metadata variables for future comparisons with TEC data
BNCdimensions$Series <- "Spoken BNC2014"
BNCdimensions$Level <- "Spoken BNC2014"
BNCdimensions$Country <- "Spoken BNC2014"
BNCdimensions$Register <- "Spoken BNC2014"
summary(BNCdimensions$Dim1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.314 23.492 25.973 26.021 28.609 40.802# Quick plot of distribution of Dim1 values:
boxplot(BNCdimensions$Dim1)
# Dimensions Biber (1988)
# Three datasets need to be uploaded because the files were so large and had to be processed by the MAT in three batches:
YFdimensionsa <- read.delim(here("data", "YF5000a_dims.tsv"), header = TRUE)
YFdimensionsb <- read.delim(here("data", "YF5000b_dims.tsv"), header = TRUE)
YFdimensionsc <- read.delim(here("data","YF5000c_dims.tsv"), header = TRUE)
# Merge the three datasets
YFdimensions <- rbind(YFdimensionsa, YFdimensionsb, YFdimensionsc)
nrow(YFdimensions)## [1] 1194YFdimensions <- YFdimensions %>% filter(Filename!="CORPUS" & Filename!=".DS_Store") # Remove mean values file
# Add metadata variables for comparison with TEC data
YFdimensions$Series <- "Youth Fiction"
YFdimensions$Level <- "Youth Fiction"
YFdimensions$Country <- "Youth Fiction"
YFdimensions$Register <- "Youth Fiction"
# Remove individual datasets no longer needed from the R environment
rm(YFdimensionsa, YFdimensionsb, YFdimensionsc)
# Check distribution of Dim scores
boxplot(YFdimensions$Dim1)
boxplot(YFdimensions$Dim2)
# Z-scores of individual features
YFzscoresa <- read.delim(here("data", "YF5000a_zscores.tsv"), header = TRUE)
YFzscoresb <- read.delim(here("data", "YF5000b_zscores.tsv"), header = TRUE)
YFzscoresc <- read.delim(here("data", "YF5000c_zscores.tsv"), header = TRUE)
YFzscores <- rbind(YFzscoresa, YFzscoresb, YFzscoresc)
YFzscores <- YFzscores %>% filter(Filename!="CORPUS") # Remove mean values rows
colnames(YFzscores)## [1] "Filename" "AMP" "ANDC" "AWL" "CAUS" "CONC"
## [7] "COND" "CONJ" "DEMO" "DEMP" "DPAR" "DWNT"
## [13] "EMPH" "EX" "FPP1" "GER" "HDG" "INPR"
## [19] "JJ" "NEMD" "NN" "NOMZ" "OSUB" "PHC"
## [25] "PIN" "PIT" "PLACE" "POMD" "PRED" "PRMD"
## [31] "RB" "SPP2" "SYNE" "THAC" "THVC" "TIME"
## [37] "TO" "TOBJ" "TPP3" "TSUB" "TTR" "VBD"
## [43] "VPRT" "XX0" "X.BEMA." "X.BYPA." "X.CONT." "X.PASS."
## [49] "X.PASTP." "X.PEAS." "X.PIRE." "X.PRESP." "X.PRIV." "X.PROD."
## [55] "X.PUBV." "X.SERE." "X.SMP." "X.SPAU." "X.SPIN." "X.STPR."
## [61] "X.SUAV." "X.THATD." "X.WHCL." "X.WHOBJ." "X.WHQU." "X.WHSUB."
## [67] "X.WZPAST." "X.WZPRES." "Underused" "Overused"MAT_features <- read.csv(here("metadata", "MAT_features_zscores_ref.csv"), header = F)
MAT_features <- MAT_features$V3 # Feature names without any spaces or hyphens
colnames(YFzscores) <- MAT_features
colnames(YFzscores)## [1] "Filename" "Amplifiers"
## [3] "Ind_clause_coord" "AWL"
## [5] "because" "Coord_conj"
## [7] "if_unless" "although_though"
## [9] "Demonstratives" "Dem_pronouns"
## [11] "Discourse_particles" "Downtoners"
## [13] "Emphatics" "Existential_there"
## [15] "FPP1" "Gerunds"
## [17] "Hedges" "Ind_pronouns"
## [19] "Attr_Adj" "Necessity_modals"
## [21] "Nouns" "Nominalizations"
## [23] "Adv_sub" "Phrasal_coord"
## [25] "Prepositions" "it"
## [27] "Place_adv" "Poss_modals"
## [29] "Pred_adj" "Pred_modals"
## [31] "Adverbs" "SPP2"
## [33] "Syn_negation" "That_adj_comp"
## [35] "That_verb_comp" "Time_adv"
## [37] "To_infinitives" "That_rel_clause_obj"
## [39] "TPP3" "That_rel_clause_subj"
## [41] "TTR" "Past_tense"
## [43] "Present_tense" "Analytic_negation"
## [45] "BE_main_verb" "By_passives"
## [47] "Contractions" "Agentless_passives"
## [49] "Past_participial_clauses" "Perfect_aspect"
## [51] "Pied_piping_relative_clauses" "Present_part_clauses"
## [53] "Private_verbs" "Pro_verb_DO"
## [55] "Public_verbs" "Sentence_relatives"
## [57] "seem_appear" "Split_auxiliaries"
## [59] "Split_infinitives" "Stranded_prepositions"
## [61] "Suasive_verbs" "That_deletion"
## [63] "WH_clauses" "WH_rel_clauses_obj"
## [65] "Direct_WH_questions" "WH_rel_clauses_subj"
## [67] "Past_part_WHIZ_del_rel" "Present_part_WHIZ_del_rel"
## [69] "Underused" "Overused"# Counts
YFcountsa <- read.delim(here("data","YF5000a_counts.tsv"), header = TRUE)
YFcountsb <- read.delim(here("data","YF5000b_counts.tsv"), header = TRUE)
YFcountsc <- read.delim(here("data","YF5000c_counts.tsv"), header = TRUE)
YFcountsc %>% filter(NNP>0) %>% select(Filename, NNP) # Tagging error, should have been added to NN count## Filename NNP
## 1 262_TheMagicBookmark_1.txt.txt 0.0236YFcountsc <- YFcountsc %>% select(-NNP) # Removing parsing error
YFcounts <- rbind(YFcountsa, YFcountsb, YFcountsc)
rm(YFcountsa, YFcountsb, YFcountsc)
YFcounts <- YFcounts %>% filter(Filename != "CORPUS" & Filename!=".DS_Store")
nrow(YFcounts)## [1] 1191colnames(YFcounts)## [1] "Filename" "Tokens" "AWL" "TTR" "X." "X..1"
## [7] "X.." "X..2" "X.LRB." "X.RRB." "." "X..3"
## [13] "AMP" "ANDC" "CAUS" "CC" "CD" "CONC"
## [19] "COND" "CONJ" "DEMO" "DEMP" "DPAR" "DT"
## [25] "DWNT" "EMPH" "EX" "FPP1" "FW" "GER"
## [31] "HDG" "IN" "INPR" "JJ" "LS" "NEMD"
## [37] "NN" "NOMZ" "OSUB" "PDT" "PHC" "PIN"
## [43] "PIT" "PLACE" "POMD" "POS" "PRED" "PRMD"
## [49] "PRP" "PRPS" "QUAN" "QUPR" "RB" "RP"
## [55] "SPP2" "SYM" "SYNE" "THAC" "THVC" "TIME"
## [61] "TO" "TOBJ" "TPP3" "TSUB" "UH" "VB"
## [67] "VBD" "VBG" "VBN" "VPRT" "WDT" "WP"
## [73] "WPS" "XX0" "X.BEMA." "X.BYPA." "X.CONT." "X.PASS."
## [79] "X.PASTP." "X.PEAS." "X.PIRE." "X.PRESP." "X.PRIV." "X.PROD."
## [85] "X.PUBV." "X.SERE." "X.SMP." "X.SPAU." "X.SPIN." "X.STPR."
## [91] "X.SUAV." "X.THATD." "X.WHCL." "X.WHOBJ." "X.WHQU." "X.WHSUB."
## [97] "X.WZPAST." "X.WZPRES." "X...1"MAT_features <- read.csv(here("metadata" ,"MAT_features.csv"), header = F)
MAT_features <- MAT_features$V3 # Feature names without any spaces or hythens
MAT_features## [1] "Filename" "Tokens"
## [3] "AWL" "TTR"
## [5] "Hashtag" "Dollar"
## [7] "Quotation" "Comma"
## [9] "LeftBracket" "RightBracket"
## [11] "Full_stop" "Colon"
## [13] "Amplifiers" "Ind_clause_coord"
## [15] "because" "Coord_conj"
## [17] "Card_numb" "although_though"
## [19] "if_unless" "Conjuncts"
## [21] "Demonstratives" "Dem_pronouns"
## [23] "Discourse_particles" "Determiners"
## [25] "Downtoners" "Emphatics"
## [27] "Existential_there" "FPP1"
## [29] "Foreign_words" "Gerunds"
## [31] "Hedges" "Prep_subord_conj"
## [33] "Ind_pronouns" "Attr_Adj"
## [35] "List_markers" "Necessity_modals"
## [37] "Nouns" "Plural_nouns"
## [39] "Nominalizations" "Adv_sub"
## [41] "Pre_determiners" "Phrasal_coord"
## [43] "Prepositions" "it"
## [45] "Place_adv" "Poss_modals"
## [47] "Poss_S" "Pred_adj"
## [49] "Pred_modals" "Personal_pronouns"
## [51] "Poss_pronouns" "Quantifiers"
## [53] "Quantifying_pronouns" "Adverbs"
## [55] "Particles" "SPP2"
## [57] "Symbols" "Syn_negation"
## [59] "That_adj_comp" "That_verb_comp"
## [61] "Time_adv" "To_infinitives"
## [63] "That_rel_clause_obj" "TPP3"
## [65] "That_rel_clause_subj" "Interjection"
## [67] "Verb_base_form" "Past_tense"
## [69] "Verb_ing_form" "Verb_past_par"
## [71] "Present_tense" "WH_determiner"
## [73] "WH_pronoun" "Poss_WH_pronoun"
## [75] "Analytic_negation" "BE_main_verb"
## [77] "By_passives" "Contractions"
## [79] "Agentless_passives" "Past_participial_clauses"
## [81] "Perfect_aspect" "Pied_piping_relative_clauses"
## [83] "Present_part_clauses" "Private_verbs"
## [85] "Pro_verb_DO" "Public_verbs"
## [87] "Sentence_relatives" "seem_appear"
## [89] "Split_auxiliaries" "Split_infinitives"
## [91] "Stranded_prepositions" "Suasive_verbs"
## [93] "That_deletion" "WH_clauses"
## [95] "WH_rel_clauses_obj" "Direct_WH_questions"
## [97] "WH_rel_clauses_subj" "Past_part_WHIZ_del_rel"
## [99] "Present_part_WHIZ_del_rel" "Quotation2"
## [101] "Register"colnames(YFcounts) <- MAT_features[c(1:37, 39:100)] # Excluding the proper noun tag
colnames(YFcounts)## [1] "Filename" "Tokens"
## [3] "AWL" "TTR"
## [5] "Hashtag" "Dollar"
## [7] "Quotation" "Comma"
## [9] "LeftBracket" "RightBracket"
## [11] "Full_stop" "Colon"
## [13] "Amplifiers" "Ind_clause_coord"
## [15] "because" "Coord_conj"
## [17] "Card_numb" "although_though"
## [19] "if_unless" "Conjuncts"
## [21] "Demonstratives" "Dem_pronouns"
## [23] "Discourse_particles" "Determiners"
## [25] "Downtoners" "Emphatics"
## [27] "Existential_there" "FPP1"
## [29] "Foreign_words" "Gerunds"
## [31] "Hedges" "Prep_subord_conj"
## [33] "Ind_pronouns" "Attr_Adj"
## [35] "List_markers" "Necessity_modals"
## [37] "Nouns" "Nominalizations"
## [39] "Adv_sub" "Pre_determiners"
## [41] "Phrasal_coord" "Prepositions"
## [43] "it" "Place_adv"
## [45] "Poss_modals" "Poss_S"
## [47] "Pred_adj" "Pred_modals"
## [49] "Personal_pronouns" "Poss_pronouns"
## [51] "Quantifiers" "Quantifying_pronouns"
## [53] "Adverbs" "Particles"
## [55] "SPP2" "Symbols"
## [57] "Syn_negation" "That_adj_comp"
## [59] "That_verb_comp" "Time_adv"
## [61] "To_infinitives" "That_rel_clause_obj"
## [63] "TPP3" "That_rel_clause_subj"
## [65] "Interjection" "Verb_base_form"
## [67] "Past_tense" "Verb_ing_form"
## [69] "Verb_past_par" "Present_tense"
## [71] "WH_determiner" "WH_pronoun"
## [73] "Poss_WH_pronoun" "Analytic_negation"
## [75] "BE_main_verb" "By_passives"
## [77] "Contractions" "Agentless_passives"
## [79] "Past_participial_clauses" "Perfect_aspect"
## [81] "Pied_piping_relative_clauses" "Present_part_clauses"
## [83] "Private_verbs" "Pro_verb_DO"
## [85] "Public_verbs" "Sentence_relatives"
## [87] "seem_appear" "Split_auxiliaries"
## [89] "Split_infinitives" "Stranded_prepositions"
## [91] "Suasive_verbs" "That_deletion"
## [93] "WH_clauses" "WH_rel_clauses_obj"
## [95] "Direct_WH_questions" "WH_rel_clauses_subj"
## [97] "Past_part_WHIZ_del_rel" "Present_part_WHIZ_del_rel"
## [99] "Quotation2"# InfoTeen Dimensions
InfoTeendimensions <- read.delim(here("data", "InfoTeen_dims.tsv"), header = TRUE)
nrow(InfoTeendimensions)## [1] 1416InfoTeendimensions <- InfoTeendimensions %>% filter(Filename!="CORPUS" & Filename!=".DS_Store")
InfoTeendimensions$Series <- "Info Teens"
InfoTeendimensions$Level <- "Info Teens"
InfoTeendimensions$Country <- "Info Teens"
InfoTeendimensions$Register <- "Info Teens"
boxplot(InfoTeendimensions$Dim1)
Dimensions <- dplyr::bind_rows(TxBdimensions, BNCdimensions, InfoTeendimensions, YFdimensions, .id = "Corpus")
nrow(Dimensions)## [1] 5805head(Dimensions); tail(Dimensions)## Corpus Filename Level Dim1 Dim2 Dim3 Dim4
## 1 1 Access_1_Informative_0001.txt A -7.3851 -4.1024 -0.9315 -6.6663
## 2 1 Access_1_Instructional_0001.txt A -4.9261 -2.4173 5.1347 -7.4853
## 3 1 Access_1_Instructional_0002.txt A -2.4381 -0.6398 4.7311 -5.6705
## 4 1 Access_1_Instructional_0003.txt A -0.1935 -3.3565 4.5138 -7.3240
## 5 1 Access_1_Instructional_0004.txt A -1.6453 -4.2051 4.6284 -7.3409
## 6 1 Access_1_Instructional_0005.txt A 4.4156 -3.2099 5.5911 -4.0795
## Dim5 Dim6 TextType Register Series Country
## 1 -3.4638 -2.9757 General narrative exposition Informative Access Germany
## 2 -3.9201 -2.1686 General narrative exposition Instructional Access Germany
## 3 -3.9201 -2.1922 General narrative exposition Instructional Access Germany
## 4 -3.9201 -1.0148 Involved persuasion Instructional Access Germany
## 5 -3.9201 -2.1161 General narrative exposition Instructional Access Germany
## 6 -3.9201 -2.5710 Involved persuasion Instructional Access Germany## Corpus Filename
## 5800 4 283_Hartnett_Thursday_Child_2.txt.txt
## 5801 4 251_TheWolfWilderKatherineRundell_3.txt.txt
## 5802 4 292_Adams_Watership_down_3.txt.txt
## 5803 4 271_Boyce_Frank-Cottrell-The-Unforgotten-Coat_4.txt.txt
## 5804 4 277_Lewis_Alice_Wonderland_2.txt.txt
## 5805 4 221_GREEN2010GHOSANCE.enuk_3.txt.txt
## Level Dim1 Dim2 Dim3 Dim4 Dim5 Dim6
## 5800 Youth Fiction 11.5351 7.7083 -0.4732 1.0855 -1.5048 -1.1593
## 5801 Youth Fiction 1.6479 6.5320 -1.0364 -1.2427 -2.6515 -2.6888
## 5802 Youth Fiction 12.5234 3.5948 -1.3319 4.3015 -1.3665 -0.1931
## 5803 Youth Fiction 9.3920 3.9129 -1.9078 -0.2747 -0.9714 -1.2909
## 5804 Youth Fiction 14.6399 5.9620 -3.4998 0.6420 -1.1585 -0.4828
## 5805 Youth Fiction 7.9830 5.6020 -0.5985 0.0463 -2.3492 -1.5609
## TextType Register Series Country
## 5800 Imaginative narrative Youth Fiction Youth Fiction Youth Fiction
## 5801 Imaginative narrative Youth Fiction Youth Fiction Youth Fiction
## 5802 Imaginative narrative Youth Fiction Youth Fiction Youth Fiction
## 5803 Imaginative narrative Youth Fiction Youth Fiction Youth Fiction
## 5804 Imaginative narrative Youth Fiction Youth Fiction Youth Fiction
## 5805 Imaginative narrative Youth Fiction Youth Fiction Youth Fiction#Convert all character vectors to factors
Dimensions[sapply(Dimensions, is.character)] <- lapply(Dimensions[sapply(Dimensions, is.character)], as.factor)
levels(Dimensions$Corpus)## [1] "1" "2" "3" "4"levels(Dimensions$Corpus) <- list(Textbook.English="1", Spoken.BNC2014="2", Informative.Teens="3", Youth.Fiction="4")
summary(Dimensions$Corpus)## Textbook.English Spoken.BNC2014 Informative.Teens Youth.Fiction
## 1949 1251 1414 1191summary(Dimensions$Series)## Access Achievers EIM GreenLine HT
## 282 238 174 206 172
## Info Teens JTT NGL POC Solutions
## 1414 175 300 92 310
## Spoken BNC2014 Youth Fiction
## 1251 1191# Re-order registers
levels(Dimensions$Register)## [1] "Conversation" "Fiction" "Info Teens" "Informative"
## [5] "Instructional" "Personal" "Poetry" "Spoken BNC2014"
## [9] "Youth Fiction"Dimensions$Register <- factor(Dimensions$Register, levels = c("Conversation", "Fiction", "Informative", "Instructional", "Personal", "Poetry", "Info Teens", "Spoken BNC2014", "Youth Fiction"))
#saveRDS(Dimensions, here("data", "Dimensions.rds"))SpokenTxBzscores <- TxBzscores %>% filter(Register=="Conversation") %>% select(-Register)
SpokenZscores <- bind_rows("Textbook Conversation" = SpokenTxBzscores, "Spoken BNC2014" = BNCzscores, .id = "Corpus") # Ignore stupid warning in R 3.x
SpokenZscores <- SpokenZscores %>% mutate_if(is.character, as.factor)
glimpse(SpokenZscores)## Rows: 1,780
## Columns: 74
## $ Corpus <fct> Textbook Conversation, Textbook Conversat…
## $ Filename <fct> Access_1_Spoken_0001.txt, Access_1_Spoken…
## $ Level <fct> A, A, A, A, A, A, A, A, A, A, A, A, A, A,…
## $ Amplifiers <dbl> -1.0385, -1.0385, -1.0385, 0.1450, -1.038…
## $ Ind_clause_coord <dbl> 1.3027, -0.4108, 1.4004, 0.9856, 1.5427, …
## $ AWL <dbl> -2.7016, -2.4668, -2.0012, -2.3692, -2.09…
## $ because <dbl> -0.6471, -0.6471, -0.6471, 0.2576, 0.2282…
## $ Coord_conj <dbl> -0.625, -0.625, -0.625, -0.625, -0.625, -…
## $ if_unless <dbl> -1.1364, -1.1364, -1.1364, -1.1364, -1.13…
## $ although_though <dbl> -0.7500, -0.7500, -0.7500, -0.7500, -0.75…
## $ Demonstratives <dbl> -2.3571, -1.1531, -2.3571, -1.9910, -2.35…
## $ Dem_pronouns <dbl> 2.4019, 1.1488, 0.0808, 0.3237, 0.9019, 0…
## $ Discourse_particles <dbl> 1.8157, 0.0278, 0.5626, 0.8161, 2.7130, 0…
## $ Downtoners <dbl> -1.2500, -1.2500, -0.4706, -1.2500, -1.25…
## $ Emphatics <dbl> -1.5000, -1.5000, -0.9062, -1.1338, -0.08…
## $ Existential_there <dbl> -0.6250, 5.0989, 0.1633, 3.0511, 1.2578, …
## $ FPP1 <dbl> 1.7593, 1.3797, 1.0599, 1.3746, 0.6683, 2…
## $ Gerunds <dbl> -1.2761, -0.8439, -0.2016, -1.0324, -0.27…
## $ Hedges <dbl> -0.4615, -0.4615, -0.4615, -0.4615, 1.827…
## $ Ind_pronouns <dbl> -0.7000, -0.7000, -0.7000, -0.7000, 0.788…
## $ Attr_Adj <dbl> -0.8265, -1.2786, -0.5758, -1.4284, -1.40…
## $ Necessity_modals <dbl> -1.0000, -1.0000, -1.0000, -1.0000, -1.00…
## $ Nouns <dbl> 3.8400, 3.7012, 4.8068, 6.0793, 2.9554, 2…
## $ Nominalizations <dbl> -1.1579, -0.9430, -1.2087, -1.3819, -1.17…
## $ Adv_sub <dbl> -0.9091, -0.9091, -0.9091, -0.9091, -0.90…
## $ Phrasal_coord <dbl> -0.4626, 4.3596, 1.9733, 1.0200, -1.2593,…
## $ Prepositions <dbl> -2.1491, -2.2102, -1.5032, -1.8670, -2.53…
## $ it <dbl> 1.2754, 0.3299, -0.5727, 0.0661, 0.8548, …
## $ Place_adv <dbl> -0.9118, -0.1682, 0.9218, -0.9118, -0.474…
## $ Poss_modals <dbl> -0.4283, 5.9283, 1.9054, 0.5406, 2.5946, …
## $ Pred_adj <dbl> 5.2231, 1.1096, 0.1108, 3.5177, -0.0908, …
## $ Pred_modals <dbl> -1.3333, -1.3333, -1.3333, 0.4981, -1.333…
## $ Adverbs <dbl> -2.3832, -1.8597, -2.1687, -2.0664, -1.44…
## $ SPP2 <dbl> 2.7889, 1.6645, 1.8125, 1.4008, 2.1941, 1…
## $ Syn_negation <dbl> -1.0625, -1.0625, -0.2831, 3.7450, -0.132…
## $ That_adj_comp <dbl> -0.5000, -0.5000, -0.5000, -0.5000, -0.50…
## $ That_verb_comp <dbl> -1.1379, -1.1379, -1.1379, -1.1379, -1.13…
## $ Time_adv <dbl> 0.6649, -0.4020, -1.1294, -1.0463, 1.0654…
## $ To_infinitives <dbl> -2.4688, -2.4350, -2.6607, -1.5618, -1.33…
## $ That_rel_clause_obj <dbl> -0.7273, -0.7273, -0.7273, -0.7273, -0.72…
## $ TPP3 <dbl> -1.0421, -0.8232, 0.2782, -0.9870, 0.5230…
## $ That_rel_clause_subj <dbl> -0.500, -0.500, -0.500, -0.500, -0.500, -…
## $ TTR <dbl> -3.5769, -2.8077, -2.6154, -2.8077, -3.04…
## $ Past_tense <dbl> -1.3191, -1.3191, -1.3191, -1.2685, -1.31…
## $ Present_tense <dbl> 1.7787, 1.0887, 0.3884, 0.7399, 0.9885, -…
## $ Analytic_negation <dbl> 0.8980, 1.0936, 1.4682, 0.6243, 1.5339, -…
## $ BE_main_verb <dbl> 5.3968, 1.5457, -0.3539, 0.0980, -0.0027,…
## $ By_passives <dbl> -0.6154, -0.6154, -0.6154, -0.6154, -0.61…
## $ Contractions <dbl> 3.4365, 1.5852, 1.1512, 1.7556, 1.3543, 0…
## $ Agentless_passives <dbl> -1.4545, -1.2630, -1.4545, -1.4545, -1.45…
## $ Past_participial_clauses <dbl> -0.2500, -0.2500, -0.2500, -0.2500, -0.25…
## $ Perfect_aspect <dbl> -1.6538, -1.6538, -1.6538, -1.6538, -1.65…
## $ Pied_piping_relative_clauses <dbl> -0.6364, -0.6364, -0.6364, -0.6364, -0.63…
## $ Present_part_clauses <dbl> -0.5882, -0.5882, -0.5882, -0.5882, -0.58…
## $ Private_verbs <dbl> -0.8003, -0.1505, -0.8915, -0.6953, -0.58…
## $ Pro_verb_DO <dbl> -0.5500, -0.4960, -0.1446, 0.4614, 1.6940…
## $ Public_verbs <dbl> -1.0276, -0.9578, -0.9641, -1.4259, -1.15…
## $ Sentence_relatives <dbl> -0.25, -0.25, -0.25, -0.25, -0.25, -0.25,…
## $ seem_appear <dbl> -0.8, -0.8, -0.8, -0.8, -0.8, -0.8, -0.8,…
## $ Split_auxiliaries <dbl> -2.2000, -2.2000, -2.2000, -2.2000, -2.20…
## $ Split_infinitives <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ Stranded_prepositions <dbl> 0.8522, 0.1956, -0.2789, -0.7407, -0.1896…
## $ Suasive_verbs <dbl> -0.5887, -0.9355, -0.5332, -0.9355, -0.93…
## $ That_deletion <dbl> -0.7561, -0.4478, -0.4520, -0.7561, -0.39…
## $ WH_clauses <dbl> -0.600, -0.600, -0.600, -0.600, -0.600, 0…
## $ WH_rel_clauses_obj <dbl> -0.8235, -0.8235, -0.8235, -0.8235, -0.82…
## $ Direct_WH_questions <dbl> 3.2517, 20.7367, 1.7450, 7.3583, 2.1467, …
## $ WH_rel_clauses_subj <dbl> -1.050, -1.050, -1.050, -1.050, -1.050, -…
## $ Past_part_WHIZ_del_rel <dbl> -0.8065, -0.8065, -0.8065, -0.8065, -0.80…
## $ Present_part_WHIZ_del_rel <dbl> -0.8889, -0.8889, -0.8889, -0.0344, 0.764…
## $ Underused <fct> AWL DEMO PIN RB TO TTR [SPAU], AWL PIN TO…
## $ Overused <fct> DEMP NN PRED SPP2 [BEMA] [CONT] [WHQU], E…
## $ Series <fct> Access, Access, Access, Access, Access, A…
## $ Country <fct> Germany, Germany, Germany, Germany, Germa…summary(SpokenZscores$Corpus)## Spoken BNC2014 Textbook Conversation
## 1251 529# This computes Dim 1 scores without the offending variables that reply on punctuation marks that are absent from the Spoken BNC2014:
SpokenNewDim1 <- SpokenZscores %>%
mutate(Dim1nopunct = Private_verbs + That_deletion + Contractions + Present_tense + SPP2 + Pro_verb_DO + Analytic_negation + Dem_pronouns + Emphatics + FPP1 + it + BE_main_verb + because + Ind_pronouns + Hedges + Amplifiers + Poss_modals + WH_clauses - Nouns - AWL - Prepositions - TTR - Attr_Adj) %>%
select(Filename, Corpus, Dim1nopunct)
SpokenNewDim1 %>%
group_by(Corpus) %>%
summarise(meanDim1 = mean(Dim1nopunct), sdDim1 = sd(Dim1nopunct))## # A tibble: 2 × 3
## Corpus meanDim1 sdDim1
## <fct> <dbl> <dbl>
## 1 Spoken BNC2014 30.7 4.61
## 2 Textbook Conversation 14.8 8.09wilcox.test(Dim1nopunct ~ Corpus, data = SpokenNewDim1, corect = TRUE, conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: Dim1nopunct by Corpus
## W = 636194, p-value < 0.00000000000000022
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 14.89054 16.23759
## sample estimates:
## difference in location
## 15.5619DimensionsNewDim1 <- merge(Dimensions, SpokenNewDim1, by = "Filename", all = TRUE)
DimensionsNewDim1$Corpus <- DimensionsNewDim1$Corpus.x
DimensionsNewDim1 <- DimensionsNewDim1 %>%
mutate(Dim1nopunct =
case_when(is.na(Dim1nopunct) ~ Dim1,
TRUE ~ Dim1nopunct))
# Checking output:
DimensionsNewDim1 %>% slice_sample(n = 10) %>% select(Corpus, Register, Dim1, Dim1nopunct)## Corpus Register Dim1 Dim1nopunct
## 1 Informative.Teens Info Teens -12.2440 -12.2440
## 2 Spoken.BNC2014 Spoken BNC2014 27.7635 29.7102
## 3 Textbook.English Instructional -6.1088 -6.1088
## 4 Youth.Fiction Youth Fiction 1.4239 1.4239
## 5 Spoken.BNC2014 Spoken BNC2014 22.8936 26.3682
## 6 Spoken.BNC2014 Spoken BNC2014 24.4378 27.3664
## 7 Youth.Fiction Youth Fiction 11.9382 11.9382
## 8 Textbook.English Fiction -1.2246 -1.2246
## 9 Informative.Teens Info Teens -14.7148 -14.7148
## 10 Informative.Teens Info Teens -21.8527 -21.8527# Add Source variable for random effect variable in mixed effect models
DimensionsNewDim1 <- DimensionsNewDim1 %>% mutate(Source = case_when(
Corpus=="Youth.Fiction" ~ paste("Book", str_extract(Filename, "[0-9]{1,3}"), sep = ""),
Corpus=="Spoken.BNC2014" ~ "Spoken.BNC2014",
Corpus=="Textbook.English" ~ as.character(Series),
Corpus=="Informative.Teens" ~ str_extract(Filename, "BBC|Science_Tech"),
TRUE ~ "NA")) %>%
mutate(Source = case_when(
Corpus=="Informative.Teens" & is.na(Source) ~ str_remove(Filename, "_.*"),
TRUE ~ as.character(Source)))
DimensionsNewDim1$Source <- as.factor(DimensionsNewDim1$Source)
summary(DimensionsNewDim1$Source, 325)## Access Achievers BBC Book1 Book10
## 282 238 100 4 4
## Book100 Book101 Book102 Book103 Book104
## 4 4 4 4 4
## Book105 Book106 Book107 Book108 Book109
## 4 4 4 4 4
## Book11 Book110 Book111 Book112 Book113
## 4 4 4 4 4
## Book114 Book115 Book116 Book117 Book118
## 4 4 4 4 4
## Book119 Book12 Book120 Book121 Book122
## 4 4 4 4 4
## Book123 Book124 Book125 Book126 Book127
## 4 4 4 4 4
## Book128 Book129 Book13 Book130 Book131
## 4 4 4 4 4
## Book132 Book133 Book134 Book135 Book136
## 4 4 4 4 4
## Book137 Book138 Book139 Book14 Book140
## 4 4 4 4 4
## Book141 Book142 Book143 Book144 Book145
## 4 4 4 4 4
## Book146 Book147 Book148 Book149 Book15
## 4 4 4 4 4
## Book150 Book151 Book152 Book153 Book154
## 4 4 4 4 4
## Book155 Book156 Book157 Book158 Book159
## 4 4 4 4 4
## Book16 Book160 Book161 Book162 Book163
## 4 4 4 4 4
## Book164 Book165 Book166 Book167 Book168
## 4 4 4 4 4
## Book169 Book17 Book170 Book171 Book172
## 4 4 4 4 4
## Book173 Book174 Book175 Book176 Book177
## 4 4 4 4 4
## Book178 Book179 Book18 Book180 Book181
## 4 4 4 4 4
## Book182 Book183 Book184 Book185 Book186
## 4 4 4 4 4
## Book187 Book188 Book189 Book19 Book190
## 4 4 4 4 4
## Book191 Book192 Book193 Book194 Book195
## 4 4 4 4 4
## Book196 Book197 Book198 Book199 Book2
## 4 4 4 4 4
## Book20 Book200 Book201 Book202 Book203
## 4 4 4 4 4
## Book204 Book205 Book206 Book207 Book208
## 4 4 4 4 4
## Book209 Book21 Book210 Book211 Book212
## 4 4 4 4 4
## Book213 Book214 Book215 Book216 Book217
## 4 4 4 4 4
## Book218 Book219 Book22 Book220 Book221
## 4 4 4 4 4
## Book222 Book223 Book224 Book225 Book226
## 4 4 4 4 4
## Book227 Book228 Book229 Book23 Book230
## 4 4 4 4 4
## Book231 Book232 Book233 Book234 Book235
## 4 4 4 4 4
## Book236 Book237 Book238 Book239 Book24
## 4 4 4 1 4
## Book240 Book241 Book242 Book243 Book244
## 4 4 4 4 4
## Book245 Book246 Book247 Book248 Book249
## 4 4 4 4 4
## Book25 Book250 Book251 Book252 Book253
## 4 4 4 4 4
## Book254 Book255 Book256 Book257 Book258
## 4 4 4 4 4
## Book259 Book26 Book260 Book261 Book262
## 4 4 4 4 1
## Book263 Book264 Book265 Book266 Book267
## 4 4 4 4 1
## Book268 Book269 Book27 Book270 Book271
## 4 4 4 4 4
## Book272 Book273 Book274 Book275 Book276
## 4 4 4 4 4
## Book277 Book278 Book279 Book28 Book280
## 4 4 4 4 4
## Book281 Book282 Book283 Book284 Book285
## 4 4 4 4 4
## Book286 Book287 Book288 Book289 Book29
## 4 4 4 4 4
## Book290 Book291 Book292 Book293 Book294
## 4 4 4 4 4
## Book295 Book296 Book297 Book298 Book299
## 4 4 4 4 4
## Book3 Book30 Book300 Book31 Book32
## 4 4 4 4 4
## Book33 Book34 Book35 Book36 Book37
## 4 4 4 4 4
## Book38 Book39 Book4 Book40 Book41
## 4 4 4 4 4
## Book42 Book43 Book44 Book45 Book46
## 4 4 4 4 4
## Book47 Book48 Book49 Book5 Book50
## 4 4 4 4 4
## Book51 Book52 Book53 Book54 Book55
## 4 4 4 4 4
## Book56 Book57 Book58 Book59 Book6
## 4 4 4 4 4
## Book60 Book61 Book62 Book63 Book64
## 4 4 4 4 4
## Book65 Book66 Book67 Book68 Book69
## 4 4 4 4 4
## Book7 Book70 Book71 Book72 Book73
## 4 4 4 4 4
## Book74 Book75 Book76 Book77 Book78
## 4 4 4 4 4
## Book79 Book8 Book80 Book81 Book82
## 4 4 4 4 4
## Book83 Book84 Book85 Book86 Book87
## 4 4 4 4 4
## Book88 Book89 Book9 Book90 Book91
## 4 4 4 4 4
## Book92 Book93 Book94 Book95 Book96
## 4 4 4 4 4
## Book97 Book98 Book99 Dogo Ducksters
## 4 4 4 100 100
## EIM Encyclopedia Factmonster GreenLine History
## 174 100 100 206 100
## HT JTT NGL POC Quatr
## 172 175 300 92 100
## Revision Science Science_Tech Solutions Spoken.BNC2014
## 100 100 100 310 1251
## Teen TeenVogue TweenTribute WhyFiles World
## 100 100 29 100 85## Re-arranging data frame
recoding_corpus <- list(
Textbook.English = "Textbook",
Spoken.BNC2014 = "Reference",
Informative.Teens = "Reference",
Youth.Fiction = "Reference")
recoding_register <- list(
`Info Teens` = "Informative",
`Spoken BNC2014` = "Conversation",
`Youth Fiction` = "Fiction")
dimensions_ref <- DimensionsNewDim1 %>%
mutate(
Corpus = recode(Corpus, !!!recoding_corpus),
Register = recode(Register, !!!recoding_register)) %>%
filter(Register %in% c("Conversation", "Fiction", "Informative")) %>%
mutate(Register = factor(Register))
dimensions_ref %>% count(Corpus, Register) # Check wrangled table is correct## Corpus Register n
## 1 Textbook Conversation 529
## 2 Textbook Fiction 285
## 3 Textbook Informative 363
## 4 Reference Conversation 1251
## 5 Reference Fiction 1191
## 6 Reference Informative 1414## Check how many different observations there are in each random effect grouping
dimensions_ref %>%
count(Corpus, Register, Source) %>%
group_by(Corpus, Register) %>%
summarize(min_n = min(n), median_n = median(n), max_n = max(n), .groups = "drop")## # A tibble: 6 × 5
## Corpus Register min_n median_n max_n
## <fct> <fct> <int> <dbl> <int>
## 1 Textbook Conversation 15 39 118
## 2 Textbook Fiction 3 28 67
## 3 Textbook Informative 12 35 79
## 4 Reference Conversation 1251 1251 1251
## 5 Reference Fiction 1 4 4
## 6 Reference Informative 29 100 100## Distributions of the dimension scores in the different combinations of corpus type and register.
dimensions_ref %>%
pivot_longer(cols = starts_with("Dim"), names_to = "Dim", values_to = "Score") %>%
ggplot(aes(x = Register, y = Score, fill = Corpus)) +
geom_point(shape = "circle filled", position = position_jitterdodge()) +
facet_wrap(vars(Dim))
#saveRDS(dimensions_ref, here("data", "AdditiveMDAdimensions_ref.rds"))Quick data import
Dimensions <- readRDS(here("data", "Dimensions.rds"))
dimensions_ref <- readRDS(here("data", "AdditiveMDAdimensions_ref.rds"))# Colours used in Register Studies paper and in the open access plots on Zenodo:
colours <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
colours2 <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
colours <- c(colours, colours2[c(2:4)]) # Nine colours range
scales::show_col(colours)
# Colours as used in Register Studies paper (see also plot on Zenodo)
london <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
#scales::show_col(london)
classic <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
#scales::show_col(classic)
colours3Reg <- c(classic[2], london[6], classic[4], london[5], london[c(2,4)])
# Colours as used in PhD thesis
colours3Reg <- c("#BD241E", "#F9B921","#267226", "#A18A33", "#15274D", "#722672")Dimension 1
Dimension 1: Visualisations
# Rainplots for all textbook registers and the three ref. corpora on Dimension 1 (not included in thesis)
ggplot(Dimensions,aes(x=Register,y=Dim1, fill = Register, colour = Register))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim1), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 1 (Biber 1988)')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
annotate(geom = "text", x = 8.3, y = -42, label = "Reference Corpora", size = 5) +
annotate(geom = "segment", x = 7, xend = 9.5, y = -39, yend = -39) +
annotate(geom = "text", x = 3.8, y = -42, label = "Textbook Corpus", size = 5) +
annotate(geom = "segment", x = 1, xend = 6.5, y = -39, yend = -39) +
#ggtitle("Dimension 1: Involved vs. Informational Discourse") +
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -45, to = 45, by = 5))
#ggsave(here("plots", "Dim1.svg"), width = 13, height = 8)# Rainplots for all textbook registers and the three ref. corpora on Dimension 1
dimensions_ref <- readRDS(here("data", "AdditiveMDAdimensions_ref.rds"))
summary(dimensions_ref)## Filename Corpus.x
## 1_BaumWizardOz_1.txt.txt : 1 Textbook.English :1177
## 1_BaumWizardOz_2.txt.txt : 1 Spoken.BNC2014 :1251
## 1_BaumWizardOz_3.txt.txt : 1 Informative.Teens:1414
## 1_BaumWizardOz_4.txt.txt : 1 Youth.Fiction :1191
## 10_Montgomery1923EmilyMoon_1.txt.txt: 1
## 10_Montgomery1923EmilyMoon_2.txt.txt: 1
## (Other) :5027
## Level Dim1 Dim2 Dim3
## Info Teens :1414 Min. :-33.259 Min. :-7.3784 Min. :-9.1267
## Spoken BNC2014:1251 1st Qu.: -8.149 1st Qu.:-2.8574 1st Qu.:-1.5764
## Youth Fiction :1191 Median : 5.164 Median :-1.1079 Median :-0.2053
## D : 285 Mean : 5.579 Mean :-0.1165 Mean : 0.7889
## C : 262 3rd Qu.: 21.191 3rd Qu.: 2.6976 3rd Qu.: 2.6426
## B : 235 Max. : 40.802 Max. :11.4408 Max. :32.1621
## (Other) : 395
## Dim4 Dim5 Dim6
## Min. :-9.2659 Min. :-3.9201 Min. :-3.4950
## 1st Qu.:-2.6768 1st Qu.:-2.1621 1st Qu.:-1.8910
## Median :-0.6504 Median :-1.3545 Median :-1.0764
## Mean :-0.7798 Mean :-0.7031 Mean :-0.9905
## 3rd Qu.: 1.0995 3rd Qu.: 0.0631 3rd Qu.:-0.1803
## Max. :25.5345 Max. :15.7538 Max. : 6.7481
##
## TextType Register Series
## Informational interaction :1496 Conversation:1780 Info Teens :1414
## Imaginative narrative :1055 Fiction :1476 Spoken BNC2014:1251
## General narrative exposition: 914 Informative :1777 Youth Fiction :1191
## Involved persuasion : 716 Solutions : 202
## Learned exposition : 655 Access : 194
## Scientific exposition : 183 NGL : 194
## (Other) : 14 (Other) : 587
## Country Corpus.y Dim1nopunct
## France : 257 Spoken BNC2014 :1251 Min. :-33.259
## Germany : 519 Textbook Conversation: 529 1st Qu.: -8.153
## Info Teens :1414 NA's :3253 Median : 5.111
## Spain : 401 Mean : 6.638
## Spoken BNC2014:1251 3rd Qu.: 23.699
## Youth Fiction :1191 Max. : 51.960
##
## Corpus Source
## Textbook :1177 Spoken.BNC2014:1251
## Reference:3856 Solutions : 202
## Access : 194
## NGL : 194
## GreenLine : 131
## Achievers : 106
## (Other) :2955dimensions_ref <- dimensions_ref %>%
unite("Subcorpus", c(Corpus, Register), remove = FALSE) %>%
mutate_if(is.character, as.factor)
dimensions_ref$Subcorpus <- fct_relevel(dimensions_ref$Subcorpus, "Reference_Conversation", "Textbook_Conversation", "Reference_Fiction", "Textbook_Fiction")
ggplot(dimensions_ref,aes(x=Subcorpus,y=Dim1, fill = Subcorpus, colour = Subcorpus))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Subcorpus)+0.25, y = Dim1), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 1 (Biber 1988)')+
theme_cowplot()+
theme(axis.title.x=element_blank())+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours3Reg)+
scale_fill_manual(values = colours3Reg) +
annotate(geom = "text", x = 1.5, y = -42, label = "Conversation", size = 5) +
annotate(geom = "segment", x = 0.7, xend = 2.5, y = -39, yend = -39) +
annotate(geom = "text", x = 3.5, y = -42, label = "Fiction", size = 5) +
annotate(geom = "segment", x = 2.7, xend = 4.5, y = -39, yend = -39) +
annotate(geom = "text", x = 5.7, y = -42, label = "Informative", size = 5) +
annotate(geom = "segment", x = 4.7, xend = 6.5, y = -39, yend = -39) +
scale_x_discrete(labels=rep(c("Reference", "Textbook"), 3))+
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -45, to = 45, by = 5))
#ggsave(here("plots", "Dim1_3RegComparison.svg"), width = 13, height = 8)
#ggsave(here("plots", "Dim1_3RegComparison.png"), width = 20, height = 15, units = "cm", dpi = 300)# Simple comparison rainplot Dim1 no punctuation-reliant features
# Colours used in Register Studies paper
bicolour <- c(classic[2], london[6])
# Colours used in PhD thesis:
bicolour <- c("#BD241E", "#F9B921")
SpokenRef <- dimensions_ref %>%
filter(Register=="Conversation")
SpokenRef$Corpus <- relevel(SpokenRef$Corpus, "Reference")
ggplot(SpokenRef, aes(x=Corpus,y=Dim1nopunct, fill = Corpus))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .5, aes(x=Corpus,y=Dim1nopunct, fill = Corpus, colour = Corpus))+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Corpus)+0.25, y = Dim1nopunct), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
theme_cowplot()+
guides(fill = "none", colour = "none") +
ylab('Adjusted Biber (1988) Dimension 1 \n(excluding punctuation-dependent variables)') +
xlab('')+
scale_x_discrete(labels=c("Spoken BNC2014", "Textbook Conversation"))+
scale_colour_manual(values = bicolour)+
scale_fill_manual(values = bicolour)+
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 50, by = 5))
#ggsave(here("plots", "SpokenDim1-nopunct.svg"), width = 8, height = 6)
#ggsave(here("plots", "SpokenDim1-nopunct.png"), width = 20, height = 15, units = "cm", dpi = 300)Testing Dim1
Comparing three textbook registers vs. three reference corpora
# Mixed effect model
# Check distribution of the outcome variable.
ggplot(dimensions_ref, aes(x = Dim1nopunct)) +
geom_histogram(bins = 20) +
facet_grid(rows = vars(Register), cols = vars(Corpus), scales = "free_y")
md0 <- lmer(Dim1nopunct ~ 1 + (Register|Source), dimensions_ref, REML = FALSE)
md_corpus <- update(md0, .~. + Corpus)
md_register <- update(md0, . ~ . + Register)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00300994 (tol = 0.002, component 1)md_both <- update(md_corpus, .~. + Register)
md_interaction <- update(md_both, . ~ . + Corpus:Register)
anova(md0, md_corpus, md_both, md_interaction)## Data: dimensions_ref
## Models:
## md0: Dim1nopunct ~ 1 + (Register | Source)
## md_corpus: Dim1nopunct ~ (Register | Source) + Corpus
## md_both: Dim1nopunct ~ (Register | Source) + Corpus + Register
## md_interaction: Dim1nopunct ~ (Register | Source) + Corpus + Register + Corpus:Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 8 32835 32887 -16409 32819
## md_corpus 9 32834 32893 -16408 32816 2.4653 1 0.1164
## md_both 11 32773 32844 -16375 32751 65.7910 2 0.000000000000005172
## md_interaction 13 32754 32838 -16364 32728 23.0039 2 0.000010110273397824
##
## md0
## md_corpus
## md_both ***
## md_interaction ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(md0, md_register)## Data: dimensions_ref
## Models:
## md0: Dim1nopunct ~ 1 + (Register | Source)
## md_register: Dim1nopunct ~ (Register | Source) + Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 8 32835 32887 -16409 32819
## md_register 10 32771 32837 -16376 32751 67.455 2 0.000000000000002251 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1md_final1 <- lmer(Dim1nopunct ~ 1 + Corpus + Register + Corpus:Register + (Register|Source), dimensions_ref)
tab_model(md_final1)| Dim 1 nopunct | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 14.65 | 12.29 – 17.02 | <0.001 |
| Corpus [Reference] | 16.01 | 8.72 – 23.30 | <0.001 |
| Register [Fiction] | -10.75 | -12.56 – -8.93 | <0.001 |
| Register [Informative] | -20.72 | -22.70 – -18.74 | <0.001 |
|
Corpus [Reference] * Register [Fiction] |
-15.48 | -22.64 – -8.33 | <0.001 |
|
Corpus [Reference] * Register [Informative] |
-22.15 | -29.71 – -14.59 | <0.001 |
| Random Effects | |||
| σ2 | 35.32 | ||
| τ00 Source | 12.34 | ||
| τ11 Source.RegisterFiction | 4.87 | ||
| τ11 Source.RegisterInformative | 7.32 | ||
| ρ01 | 0.40 | ||
| 0.12 | |||
| ICC | 0.35 | ||
| N Source | 325 | ||
| Observations | 5033 | ||
| Marginal R2 / Conditional R2 | 0.829 / 0.889 | ||
# The reference levels are `CorpusTextbook` and `RegisterConversation`. The estimate for `CorpusReference` is positive, since `Dim1nopunct` values are higher for `Reference` texts in the `Conversation` register. The estimates for `RegisterFiction` and `RegisterInformative` are negative, since `Dim1` values are generally lower in these two registers.
ranef(md_final1)## $Source
## (Intercept) RegisterFiction
## Access -0.76244356309470196 1.658131412388347048
## Achievers -0.27292168586377225 -1.746777523614002181
## BBC -1.48451072245553073 0.013613910897331827
## Book1 1.56858377181185560 0.808150163526446752
## Book10 -1.17679073167432491 -0.606294441731024314
## Book100 -4.08057225803153933 -2.102351941203952634
## Book101 -0.04599447611808670 -0.023696817514062628
## Book102 -1.47092730944075067 -0.757836570173723967
## Book103 2.19972141274745159 1.133318635173096389
## Book104 2.29814210269453323 1.184025966272949315
## Book105 1.29592733002658322 0.667674817564663847
## Book106 -3.09785005023388571 -1.596043558314470756
## Book107 4.52201865598057129 2.329789573227002197
## Book108 -0.63054392914807389 -0.324862585351766708
## Book109 -3.49936336126617986 -1.802907261611608858
## Book11 -3.37502043363838800 -1.738844532478764915
## Book110 -1.49417949677374717 -0.769816331365440920
## Book111 -0.48989333975532773 -0.252397984569531553
## Book112 -2.84457841343940521 -1.465555459195834187
## Book113 -1.10070028342448234 -0.567091876142269302
## Book114 -3.68352499533869393 -1.897789191009034315
## Book115 4.66096903394114559 2.401378207948874799
## Book116 -0.37975076958218851 -0.195651422673235820
## Book117 -3.63227918597451982 -1.871386833696702112
## Book118 3.20314549072983690 1.650292830163751612
## Book119 1.63438913822902987 0.842053751327516586
## Book12 -2.30167216717778222 -1.185844691061936462
## Book120 -0.51048745006055729 -0.263008277695048087
## Book121 2.53380098585006452 1.305439797259239976
## Book122 -6.01759201207765315 -3.100324034959673902
## Book123 -0.59616852758626504 -0.307152032117256113
## Book124 -0.62649694700669833 -0.322777539377101075
## Book125 1.38709218808705370 0.714643870970294470
## Book126 -1.98982704921284870 -1.025178944286307070
## Book127 -1.44682501639166605 -0.745418825951825625
## Book128 1.58625639205053015 0.817255275534157555
## Book129 2.61045418013731867 1.344932295276424661
## Book13 -4.24757612462301193 -2.188394015921805824
## Book130 -0.24074052502189336 -0.124031944075998379
## Book131 0.28764208414656556 0.148196099894380873
## Book132 -1.38522228527515323 -0.713680478201370372
## Book133 -1.46180363964273674 -0.753135963568117073
## Book134 3.28645585090645653 1.693215167121510323
## Book135 1.26460512505072198 0.651537301973762251
## Book136 0.73354049221460527 0.377927452387036178
## Book137 -0.18111339169628918 -0.093311444212583597
## Book138 -1.41726288944770662 -0.730188120296650811
## Book139 0.59782291063915793 0.308004386934866703
## Book14 -2.45283533148215094 -1.263725476358243505
## Book140 -0.14506172534219311 -0.074737262467856447
## Book141 0.59581139288841412 0.306968032722607143
## Book142 0.85556059577308585 0.440793439155723221
## Book143 1.40360339629107189 0.723150611795926990
## Book144 2.95272350408224460 1.521272899512483123
## Book145 0.99684577112291228 0.513584985016825701
## Book146 -2.26049591988726961 -1.164630273585976150
## Book147 1.46440391497127642 0.754475651616493215
## Book148 -0.09125362550981052 -0.047014787289907572
## Book149 0.65692321788718644 0.338453460695070651
## Book15 -3.07307725126788700 -1.583280362688485710
## Book150 0.76468309739129081 0.393972436351844202
## Book151 1.35201036064849545 0.696569360006478289
## Book152 1.67603234499739395 0.863508751031322741
## Book153 0.75421841575944781 0.388580926938064342
## Book154 1.05227026914785649 0.542140244829626861
## Book155 3.63999206891953175 1.875360588701238118
## Book156 1.83593603286153928 0.945892742130901798
## Book157 1.26110891562681160 0.649736019652453312
## Book158 3.06696094967654043 1.580129182483735573
## Book159 -4.75286219961775647 -2.448722492776416182
## Book16 2.78989832615154265 1.437383727295098224
## Book160 0.87577155984007793 0.451206331478904654
## Book161 0.29567618182959371 0.152335347968346912
## Book162 0.62681031822576283 0.322938991386538865
## Book163 2.68561070943145941 1.383653696409487877
## Book164 -2.73780034617078316 -1.410542323095044681
## Book165 -0.55178343055051871 -0.284284382921738410
## Book166 0.08469431108050204 0.043635362407457495
## Book167 5.73246143581028988 2.953421889229392416
## Book168 0.70593001642166486 0.363702257068756363
## Book169 -0.41604190233518162 -0.214348979919422644
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## Book172 1.65780895204125467 0.854119875370255466
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## Book178 1.49857577009402254 0.772081335674763336
## Book179 0.46438025985324111 0.239253388746562268
## Book18 -2.23296925733394325 -1.150448259693206854
## Book180 2.93084824854291126 1.510002547454158517
## Book181 1.33013510510916233 0.685299007948153127
## Book182 -0.25976613041434049 -0.133834127666955405
## Book183 2.20909652226430886 1.138148786055235284
## Book184 -5.72056986420450642 -2.947295231724387676
## Book185 7.98139814774100476 4.112096742411181616
## Book186 1.06870963743220448 0.550609973004821174
## Book187 1.44030162192219180 0.742057907394594762
## Book188 -1.58420688943945431 -0.816199351139138773
## Book189 1.05136029683204391 0.541671417924080467
## Book19 0.83010531757022743 0.427678623350578713
## Book190 -3.73334597962942549 -1.923457464087682478
## Book191 2.84535874413632017 1.465957493433118319
## Book192 2.18637116100888784 1.126440450966728957
## Book193 -0.62377900337920567 -0.321377227435535928
## Book194 -2.64037344177912692 -1.360347000326248335
## Book195 2.43573949550132962 1.254917529411576194
## Book196 -4.62657959415889319 -2.383660422081750774
## Book197 -1.92393786955606116 -0.991232175059726606
## Book198 -0.63855408019121296 -0.328989495875586846
## Book199 -0.39101766365034002 -0.201456240016905430
## Book2 3.17009912768191393 1.633267010962340837
## Book20 -0.24167444397759577 -0.124513108531690161
## Book200 0.08643044247251248 0.044529834793039083
## Book201 -4.24733665822411410 -2.188270640420346247
## Book202 1.99774347859692458 1.029257568467083939
## Book203 -1.06164331376421717 -0.546969331854225360
## Book204 4.48815810717639341 2.312344277320629526
## Book205 1.94429457836288933 1.001720156541324069
## Book206 -2.27589360933641194 -1.172563318329821858
## Book207 2.45384315525801844 1.264244717321913791
## Book208 0.06426782725450225 0.033111432132962540
## Book209 2.19466867173070401 1.130715412092301086
## Book21 4.38110465354904033 2.257189259393161507
## Book210 0.00635287868101807 0.003273067104983272
## Book211 -2.95754668711954283 -1.523757852009351765
## Book212 3.55658592218335556 1.832388901542895354
## Book213 1.24174805727590742 0.639761110359452712
## Book214 1.10648546185836594 0.570072458360056600
## Book215 2.03633348877933962 1.049139530527281483
## Book216 2.63017423808656847 1.355092267821614627
## Book217 -4.01748483524185929 -2.069848665344448335
## Book218 0.22059149245512288 0.113650959485721653
## Book219 1.15659380582777516 0.595888782040456144
## Book22 -2.08388945069998721 -1.073640841259597467
## Book220 2.60596418515798067 1.342619004624059365
## Book221 0.97008540104605956 0.499797772728727607
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## Book224 -0.47389698430894078 -0.244156501072037208
## Book225 1.87979430381970958 0.968488965223209153
## Book226 3.11503382925531591 1.604896814401730154
## Book227 1.79508306520953909 0.924844881581911915
## Book228 0.10853319109079904 0.055917393577750441
## Book229 -1.82478680709234053 -0.940148548680421969
## Book23 3.41344488224207021 1.758641195545481040
## Book230 4.95824262153313455 2.554536555460693581
## Book231 -0.49243168358364714 -0.253705764885002016
## Book232 3.67884549214073564 1.895378263813041197
## Book233 -2.57140711889650087 -1.324814855905913413
## Book234 -1.65866896617679060 -0.854562963317969215
## Book235 2.83205638567753581 1.459103984327043246
## Book236 0.54673275443426717 0.281682223698483281
## Book237 0.30566193066364089 0.157480106379208007
## Book238 -2.24440377788132306 -1.156339439887897891
## Book239 4.23253087914970294 2.180642553865363098
## Book24 -1.48733075776526458 -0.766287792023699454
## Book240 -5.40486933721731511 -2.784643139375260645
## Book241 -4.09511984176459443 -2.109847002917616621
## Book242 -1.33845449757037183 -0.689585242766330375
## Book243 -1.26032858492989597 -0.649333985415169845
## Book244 -1.75550917789114602 -0.904456016108189598
## Book245 -1.89159793238488483 -0.974570313587621584
## Book246 0.28340352888607151 0.146012353518547799
## Book247 -3.22154641858465318 -1.659773173593373619
## Book248 -1.74557132233687851 -0.899335932797620474
## Book249 -0.90485268908574445 -0.466189222273620141
## Book25 2.60978367422040414 1.344586843872338289
## Book250 0.27216058145780925 0.140219873725024424
## Book251 0.36690546218180736 0.189033390877474172
## Book252 -1.81124498223465813 -0.933171664072887541
## Book253 3.91266048402475208 2.015842103438093780
## Book254 0.51378217794590186 0.264705754697656226
## Book255 -7.43699317158577244 -3.831613813535621027
## Book256 -10.10934239672800672 -5.208435059623019114
## Book257 2.09392515771430920 1.078811338628290928
## Book258 -1.05232807084708480 -0.542170024847451026
## Book259 -0.68072411303715197 -0.350715921682604348
## Book26 3.70843156572458321 1.910621307018362192
## Book260 1.80936723590380510 0.932204230243970478
## Book261 3.59500830588654052 1.852184500752071861
## Book262 -3.19121562510544177 -1.644146443194526075
## Book263 -2.92794864021574819 -1.508508640028957792
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## Book265 -3.54501763021608518 -1.826428800964861709
## Book266 -0.56691770696087274 -0.292081714613978582
## Book267 -0.04490885961860926 -0.023137496955386708
## Book268 -0.21002895936322416 -0.108209036013817733
## Book269 3.51181767890937202 1.809323851545042938
## Book27 -4.10248343353070677 -2.113640799587496844
## Book270 1.81931706477801747 0.937330482329613024
## Book271 2.45470523429405274 1.264688869127167825
## Book272 4.26429294416658866 2.197006689781219713
## Book273 -0.60208334763904625 -0.310199407003305394
## Book274 0.00046200526812710 0.000238029769079616
## Book275 3.42313129807749394 1.763631734579518273
## Book276 -1.54846652940392637 -0.797785557546308977
## Book277 3.68103660969065194 1.896507149651396329
## Book278 0.47249817077588363 0.243435818246039548
## Book279 -5.54641792560592783 -2.857570398287969216
## Book28 2.75843244133634480 1.421172186403320215
## Book280 2.46866612534980678 1.271881660862256513
## Book281 -0.64686356423297353 -0.333270625776231344
## Book282 0.42159958769011180 0.217212355410823510
## Book283 1.95449584695594480 1.006975952903498506
## Book284 1.34990305633819263 0.695483655593635230
## Book285 2.43953503792386384 1.256873031109709160
## Book286 2.37997974451792782 1.226189543896732737
## Book287 4.09677422481748810 2.110699357735227544
## Book288 3.82110050640609833 1.968669480455054632
## Book289 4.22687631933877483 2.177729267678172320
## Book29 -2.17702990655136830 -1.121627742552268714
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## Book291 2.33474454176609392 1.202883911671033257
## Book292 -0.05266361532739638 -0.027132825229709483
## Book293 -0.79047156365211035 -0.407258914001491501
## Book294 1.28827637858179078 0.663732970293033131
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## Book296 -5.94995472770890999 -3.065476624572439857
## Book297 1.40953018966379817 0.726204155457049749
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## Book3 -5.81541053148806419 -2.996158099077422143
## Book30 3.34168877581228063 1.721671726533143598
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## Book31 -0.44887274562409912 -0.231263761169519605
## Book32 3.06300975409472187 1.578093486709654325
## Book33 2.04807931564528634 1.055191098873870192
## Book34 3.60459893516240593 1.857125689585524153
## Book35 -0.19795985285876405 -0.101990910740259094
## Book36 5.39916012850409199 2.781701697522993122
## Book37 3.53091512422149023 1.819163047786437115
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## Book39 -1.98854590397874453 -1.024518885353498776
## Book4 0.22772759114228366 0.117327549429215056
## Book40 0.08268279332975945 0.042599008195197699
## Book41 -1.38421652639978121 -0.713162301095240148
## Book42 1.25485884261557290 0.646515919064361011
## Book43 1.31409085638299583 0.677032849350367116
## Book44 5.39611890523808668 2.780134828654456491
## Book45 -1.87363795246753395 -0.965317150978159177
## Book46 0.24532837146128728 0.126395648786487680
## Book47 1.12017096655538761 0.577123368268466663
## Book48 -6.09270064809201273 -3.139020760992445869
## Book49 3.13083861158258481 1.613039597498056699
## Book5 0.83788797553441297 0.431688327148012252
## Book50 0.37163492356004224 0.191470057031299212
## Book51 0.53980020218617009 0.278110502931230630
## Book52 -3.19821041801204231 -1.647750230976146035
## Book53 -0.52091621173256553 -0.268381280783609011
## Book54 1.73547987852382435 0.894136719268643354
## Book55 1.77398607546662412 0.913975499903330491
## Book56 -0.50242940573763872 -0.258856692070936034
## Book57 1.36404354719312071 0.702768978954817825
## Book58 0.09884677525537422 0.050926854543713970
## Book59 -0.58235131636984960 -0.300033265683043004
## Book6 -4.69804834091000334 -2.420481840492337611
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## Book63 0.04295531775257873 0.022131012503067420
## Book64 -1.13906280052794351 -0.586856631476080581
## Book65 -1.64797679146599441 -0.849054247177802868
## Book66 0.75618204023041213 0.389592606050031875
## Book67 1.10928721872547342 0.571515951727132432
## Book68 2.05408992225762610 1.058287823960503138
## Book69 -0.26685433582172147 -0.137486042510156281
## Book7 0.61402281252460822 0.316350739608601828
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## Book71 -0.02307754174354709 -0.011889782024388940
## Book72 -5.07019109811745583 -2.612213538535468338
## Book73 1.16200574644287036 0.598677068373440591
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##
## with conditional variances for "Source"# check distribution of residuals
plot(md_final1)
# This function can be used to estimate mean and sd of all the terms of the model
library(merTools)## Loading required package: arm## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked _by_ '.GlobalEnv':
##
## select## The following object is masked from 'package:dplyr':
##
## select##
## arm (Version 1.11-2, built: 2020-7-27)## Working directory is /Users/Elen/Documents/PhD/GitHubRepo/6_7_MDAs##
## Attaching package: 'arm'## The following object is masked from 'package:scales':
##
## rescale## The following object is masked from 'package:car':
##
## logit(randomSims <- REsim(md_final1, n.sims = 500))## groupFctr groupID term mean median
## 1 Source Access (Intercept) -0.738954439 -0.6852231098
## 2 Source Achievers (Intercept) -0.272026472 -0.2975384152
## 3 Source BBC (Intercept) -1.490354619 -1.5352112256
## 4 Source Book1 (Intercept) 1.498175293 1.5680681891
## 5 Source Book10 (Intercept) -1.330456433 -1.3600749799
## 6 Source Book100 (Intercept) -4.195233395 -4.1959301576
## 7 Source Book101 (Intercept) 0.009925686 0.0024220479
## 8 Source Book102 (Intercept) -1.364628531 -1.2883309234
## 9 Source Book103 (Intercept) 2.213601727 2.3528064416
## 10 Source Book104 (Intercept) 2.364239625 2.2279685226
## 11 Source Book105 (Intercept) 1.405088882 1.3465905005
## 12 Source Book106 (Intercept) -3.050055634 -3.1917778204
## 13 Source Book107 (Intercept) 4.420694673 4.4418873641
## 14 Source Book108 (Intercept) -0.589192797 -0.6130686890
## 15 Source Book109 (Intercept) -3.423418587 -3.4207400150
## 16 Source Book11 (Intercept) -3.300515697 -3.3373909920
## 17 Source Book110 (Intercept) -1.478295082 -1.6080043950
## 18 Source Book111 (Intercept) -0.355600620 -0.4061290006
## 19 Source Book112 (Intercept) -2.787846366 -2.8490328732
## 20 Source Book113 (Intercept) -1.046683442 -1.1452619662
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## 27 Source Book12 (Intercept) -2.233690922 -2.3079143209
## 28 Source Book120 (Intercept) -0.497310362 -0.4774552451
## 29 Source Book121 (Intercept) 2.432573774 2.5005822706
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## 61 Source Book150 (Intercept) 0.786713311 0.7998684289
## 62 Source Book151 (Intercept) 1.333745265 1.3977056940
## 63 Source Book152 (Intercept) 1.536867516 1.4835327655
## 64 Source Book153 (Intercept) 0.753105215 0.7727520505
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## 71 Source Book16 (Intercept) 2.807757534 2.7024694298
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## 756 Source Book191 RegisterInformative -0.467692362 -0.4275883090
## 757 Source Book192 RegisterInformative -0.412487504 -0.5119842835
## 758 Source Book193 RegisterInformative 0.114921695 0.0202997366
## 759 Source Book194 RegisterInformative 0.316877356 0.3299754704
## 760 Source Book195 RegisterInformative -0.198849186 -0.2401340557
## 761 Source Book196 RegisterInformative 0.800950193 0.7221514299
## 762 Source Book197 RegisterInformative 0.388811033 0.4002308057
## 763 Source Book198 RegisterInformative 0.143651850 0.2216239734
## 764 Source Book199 RegisterInformative 0.156792198 0.0583332963
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## 768 Source Book201 RegisterInformative 0.497975492 0.4817895733
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## 771 Source Book204 RegisterInformative -0.569034892 -0.6929099423
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## 792 Source Book223 RegisterInformative -0.028535880 -0.0929941408
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## 796 Source Book227 RegisterInformative -0.071117675 0.0346809555
## 797 Source Book228 RegisterInformative 0.020341639 -0.0678769950
## 798 Source Book229 RegisterInformative 0.324072953 0.2716720505
## 799 Source Book23 RegisterInformative -0.332178942 -0.4648207727
## 800 Source Book230 RegisterInformative -0.730137115 -0.7994106204
## 801 Source Book231 RegisterInformative 0.054129399 0.0826036551
## 802 Source Book232 RegisterInformative -0.553390837 -0.5559228706
## 803 Source Book233 RegisterInformative 0.432684066 0.3988676149
## 804 Source Book234 RegisterInformative 0.172859144 0.1830600094
## 805 Source Book235 RegisterInformative -0.178747061 -0.1548455627
## 806 Source Book236 RegisterInformative -0.146275751 -0.2505678685
## 807 Source Book237 RegisterInformative -0.073606466 -0.1169048910
## 808 Source Book238 RegisterInformative 0.214216183 0.3245141395
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## 810 Source Book24 RegisterInformative 0.140800737 0.0371412786
## 811 Source Book240 RegisterInformative 0.632422646 0.6159387902
## 812 Source Book241 RegisterInformative 0.698280523 0.6682280171
## 813 Source Book242 RegisterInformative 0.144800294 -0.0288934246
## 814 Source Book243 RegisterInformative 0.315690265 0.3309752457
## 815 Source Book244 RegisterInformative 0.326260666 0.3716128058
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## 818 Source Book247 RegisterInformative 0.339535151 0.3789258029
## 819 Source Book248 RegisterInformative 0.149795610 0.1290999875
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## 822 Source Book250 RegisterInformative -0.002476937 0.0342979712
## 823 Source Book251 RegisterInformative -0.301982190 -0.3532201043
## 824 Source Book252 RegisterInformative 0.116714681 0.0542022239
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## 826 Source Book254 RegisterInformative -0.016605575 0.0579875529
## 827 Source Book255 RegisterInformative 1.163870722 1.2446403876
## 828 Source Book256 RegisterInformative 1.380954589 1.3150608242
## 829 Source Book257 RegisterInformative -0.253301011 -0.2508771350
## 830 Source Book258 RegisterInformative 0.064658404 0.0257129490
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## 836 Source Book263 RegisterInformative 0.360565376 0.5222828459
## 837 Source Book264 RegisterInformative 0.188547022 0.3025145995
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## 843 Source Book27 RegisterInformative 0.471659629 0.5047098009
## 844 Source Book270 RegisterInformative -0.212769875 -0.2382297861
## 845 Source Book271 RegisterInformative -0.257662381 -0.2782103736
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## 847 Source Book273 RegisterInformative 0.215316839 0.1605455667
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## 850 Source Book276 RegisterInformative 0.293083948 0.3131659368
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## 853 Source Book279 RegisterInformative 0.770366931 0.7195963940
## 854 Source Book28 RegisterInformative -0.275002347 -0.2182289059
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## 878 Source Book300 RegisterInformative 0.597208363 0.6066022234
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## 883 Source Book35 RegisterInformative 0.211424244 0.3076670496
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## 886 Source Book38 RegisterInformative 0.832492768 0.8839003385
## 887 Source Book39 RegisterInformative 0.247932756 0.1328302344
## 888 Source Book4 RegisterInformative -0.117814626 -0.1135398414
## 889 Source Book40 RegisterInformative -0.002309198 -0.0396364411
## 890 Source Book41 RegisterInformative -0.016069474 0.0454374031
## 891 Source Book42 RegisterInformative -0.043789495 -0.1113775198
## 892 Source Book43 RegisterInformative -0.067734995 -0.0884226553
## 893 Source Book44 RegisterInformative -0.706053200 -0.7369427849
## 894 Source Book45 RegisterInformative 0.470863563 0.5124665466
## 895 Source Book46 RegisterInformative -0.059754928 -0.0779050063
## 896 Source Book47 RegisterInformative -0.210873793 -0.1977215397
## 897 Source Book48 RegisterInformative 0.861093842 0.7559270014
## 898 Source Book49 RegisterInformative -0.483583710 -0.4983115044
## 899 Source Book5 RegisterInformative -0.102770159 -0.1772189070
## 900 Source Book50 RegisterInformative -0.051715501 -0.1273257395
## 901 Source Book51 RegisterInformative -0.059130650 -0.0586517909
## 902 Source Book52 RegisterInformative 0.459122872 0.3759739676
## 903 Source Book53 RegisterInformative -0.068010764 -0.1630077181
## 904 Source Book54 RegisterInformative -0.330142147 -0.3536237984
## 905 Source Book55 RegisterInformative -0.308960951 -0.3600826691
## 906 Source Book56 RegisterInformative 0.002846205 0.0154659620
## 907 Source Book57 RegisterInformative -0.058796612 -0.1349012113
## 908 Source Book58 RegisterInformative -0.031844143 -0.0779926998
## 909 Source Book59 RegisterInformative 0.214824912 0.3625304017
## 910 Source Book6 RegisterInformative 0.635949600 0.5853487310
## 911 Source Book60 RegisterInformative 0.498094686 0.6229964346
## 912 Source Book61 RegisterInformative 0.081960431 0.1170187586
## 913 Source Book62 RegisterInformative -0.075455073 -0.1211639689
## 914 Source Book63 RegisterInformative -0.135224779 -0.0375457615
## 915 Source Book64 RegisterInformative 0.185796574 0.1493612038
## 916 Source Book65 RegisterInformative 0.198926857 0.2641437991
## 917 Source Book66 RegisterInformative -0.069080632 0.0188153927
## 918 Source Book67 RegisterInformative -0.026187627 -0.0160995970
## 919 Source Book68 RegisterInformative -0.309803624 -0.3482745824
## 920 Source Book69 RegisterInformative 0.045627158 0.1083237727
## 921 Source Book7 RegisterInformative -0.080986058 -0.0272586912
## 922 Source Book70 RegisterInformative 0.295907655 0.3471591364
## 923 Source Book71 RegisterInformative 0.044373477 -0.0737360709
## 924 Source Book72 RegisterInformative 0.441358512 0.3107308300
## 925 Source Book73 RegisterInformative -0.272615236 -0.2785027784
## 926 Source Book74 RegisterInformative -0.136613370 -0.2972280046
## 927 Source Book75 RegisterInformative 0.136283059 0.0881998425
## 928 Source Book76 RegisterInformative 1.212224310 1.1458100349
## 929 Source Book77 RegisterInformative 0.632082990 0.6941452902
## 930 Source Book78 RegisterInformative -0.539320837 -0.4143147924
## 931 Source Book79 RegisterInformative 0.179109135 0.1682957011
## 932 Source Book8 RegisterInformative -0.245201264 -0.1734773436
## 933 Source Book80 RegisterInformative -0.820831059 -0.8107559995
## 934 Source Book81 RegisterInformative 0.327908082 0.2591047821
## 935 Source Book82 RegisterInformative -0.099982602 -0.1381288263
## 936 Source Book83 RegisterInformative 0.370163293 0.3981030756
## 937 Source Book84 RegisterInformative -0.318536462 -0.2522495068
## 938 Source Book85 RegisterInformative -0.754756418 -0.7559486923
## 939 Source Book86 RegisterInformative 0.208454708 0.1890697241
## 940 Source Book87 RegisterInformative 0.229617397 0.1343008796
## 941 Source Book88 RegisterInformative 0.635793593 0.5436905777
## 942 Source Book89 RegisterInformative 0.464030314 0.5084559112
## 943 Source Book9 RegisterInformative 0.657754833 0.6539115902
## 944 Source Book90 RegisterInformative -0.137134179 -0.0893880420
## 945 Source Book91 RegisterInformative -0.222622976 -0.1744997842
## 946 Source Book92 RegisterInformative -0.248724413 -0.1322318275
## 947 Source Book93 RegisterInformative -0.385023618 -0.4771781856
## 948 Source Book94 RegisterInformative -0.262736521 -0.3246328159
## 949 Source Book95 RegisterInformative -0.208486458 -0.1274984683
## 950 Source Book96 RegisterInformative -0.055337345 -0.1356168133
## 951 Source Book97 RegisterInformative 0.934099190 0.9209802723
## 952 Source Book98 RegisterInformative 0.280051754 0.2491097030
## 953 Source Book99 RegisterInformative -0.317900168 -0.3044575383
## 954 Source Dogo RegisterInformative -0.670391695 -0.7496301170
## 955 Source Ducksters RegisterInformative -1.743683603 -1.7729557246
## 956 Source EIM RegisterInformative 1.237321949 1.1749446823
## 957 Source Encyclopedia RegisterInformative -0.468485018 -0.4884756132
## 958 Source Factmonster RegisterInformative -3.550203181 -3.4712782120
## 959 Source GreenLine RegisterInformative 1.067112590 1.0157536622
## 960 Source History RegisterInformative -1.386587783 -1.3985205183
## 961 Source HT RegisterInformative -4.275797965 -4.3110539287
## 962 Source JTT RegisterInformative -2.999644056 -3.0520810724
## 963 Source NGL RegisterInformative -0.853514790 -0.8024717043
## 964 Source POC RegisterInformative 0.475832662 0.5308849098
## 965 Source Quatr RegisterInformative 0.458318078 0.4318709564
## 966 Source Revision RegisterInformative 0.210556674 0.2278987316
## 967 Source Science RegisterInformative 0.065236175 0.0897473194
## 968 Source Science_Tech RegisterInformative -0.178925983 -0.0795799547
## 969 Source Solutions RegisterInformative 2.480714848 2.5009172615
## 970 Source Spoken.BNC2014 RegisterInformative 0.075535409 0.0414710534
## 971 Source Teen RegisterInformative 2.751041458 2.8125067256
## 972 Source TeenVogue RegisterInformative 3.325165816 3.3345910043
## 973 Source TweenTribute RegisterInformative 1.878663801 1.8220531648
## 974 Source WhyFiles RegisterInformative 0.783654727 0.7160627460
## 975 Source World RegisterInformative -0.408280104 -0.2881024798
## sd
## 1 1.290196
## 2 1.359719
## 3 2.510588
## 4 2.009644
## 5 2.005272
## 6 1.971217
## 7 2.115101
## 8 2.048888
## 9 2.016616
## 10 2.033484
## 11 2.028153
## 12 2.080875
## 13 2.025305
## 14 1.960662
## 15 1.969154
## 16 1.997946
## 17 1.941083
## 18 2.005896
## 19 2.074364
## 20 1.955372
## 21 2.044805
## 22 2.009814
## 23 2.097194
## 24 2.044394
## 25 2.047731
## 26 2.016328
## 27 2.167799
## 28 2.011712
## 29 2.044992
## 30 1.976326
## 31 2.026344
## 32 1.949097
## 33 1.942944
## 34 2.011134
## 35 2.064394
## 36 1.892210
## 37 2.099910
## 38 2.055091
## 39 2.021516
## 40 2.089210
## 41 2.047276
## 42 1.920610
## 43 2.084027
## 44 2.051698
## 45 2.061696
## 46 2.029176
## 47 1.946341
## 48 2.060504
## 49 2.034451
## 50 2.068533
## 51 2.049414
## 52 1.912158
## 53 2.049152
## 54 2.051001
## 55 2.050117
## 56 2.040027
## 57 2.112108
## 58 2.100282
## 59 1.950367
## 60 2.048373
## 61 1.983745
## 62 1.995542
## 63 1.963661
## 64 2.070975
## 65 2.024492
## 66 2.088109
## 67 1.971763
## 68 1.952851
## 69 2.042823
## 70 2.123959
## 71 2.042184
## 72 2.100306
## 73 2.092798
## 74 1.986585
## 75 2.002818
## 76 2.038192
## 77 1.947067
## 78 2.000182
## 79 2.005480
## 80 2.036312
## 81 1.958125
## 82 2.034941
## 83 2.050019
## 84 1.976223
## 85 1.934188
## 86 1.959658
## 87 2.025181
## 88 2.051625
## 89 2.069451
## 90 2.035398
## 91 1.970898
## 92 2.025794
## 93 1.914719
## 94 1.943866
## 95 2.045348
## 96 2.110767
## 97 2.079281
## 98 2.101888
## 99 1.999373
## 100 2.067583
## 101 2.005627
## 102 2.035437
## 103 2.017328
## 104 2.068789
## 105 2.046501
## 106 2.069348
## 107 1.998190
## 108 1.999178
## 109 2.106439
## 110 2.005990
## 111 1.943112
## 112 2.062106
## 113 1.977453
## 114 2.044890
## 115 1.916464
## 116 2.013834
## 117 2.028857
## 118 1.896021
## 119 1.862892
## 120 2.048383
## 121 2.162567
## 122 1.970734
## 123 2.004845
## 124 1.993258
## 125 2.004763
## 126 1.994322
## 127 1.996041
## 128 2.034655
## 129 2.062160
## 130 2.055643
## 131 2.039483
## 132 1.911981
## 133 2.000252
## 134 2.119595
## 135 1.904832
## 136 2.104795
## 137 1.923315
## 138 2.040870
## 139 1.899731
## 140 2.075167
## 141 2.090183
## 142 2.020349
## 143 1.882679
## 144 1.988483
## 145 1.861661
## 146 2.029946
## 147 1.971002
## 148 2.089888
## 149 2.107564
## 150 2.077047
## 151 2.002007
## 152 2.043856
## 153 1.989553
## 154 2.008169
## 155 2.087345
## 156 1.963013
## 157 2.131638
## 158 2.067822
## 159 2.873881
## 160 2.113750
## 161 2.045114
## 162 2.048263
## 163 2.028369
## 164 2.002591
## 165 1.933058
## 166 2.095563
## 167 1.958060
## 168 2.039769
## 169 2.058966
## 170 2.092580
## 171 2.134106
## 172 1.944146
## 173 1.966949
## 174 2.039747
## 175 2.013729
## 176 2.072513
## 177 2.028496
## 178 2.026584
## 179 2.061238
## 180 1.923340
## 181 2.126789
## 182 2.134291
## 183 1.991895
## 184 1.965668
## 185 2.958981
## 186 2.065338
## 187 1.907228
## 188 1.964171
## 189 2.030547
## 190 3.033967
## 191 2.078220
## 192 1.954203
## 193 1.943176
## 194 1.952785
## 195 2.054621
## 196 2.143379
## 197 2.081906
## 198 1.967058
## 199 2.047059
## 200 1.983262
## 201 1.986814
## 202 1.993058
## 203 2.064306
## 204 2.055255
## 205 2.092959
## 206 2.090873
## 207 2.044723
## 208 1.923282
## 209 2.097893
## 210 2.082091
## 211 2.142074
## 212 2.095931
## 213 2.015362
## 214 2.020303
## 215 2.009579
## 216 2.026344
## 217 2.028198
## 218 2.004641
## 219 2.018059
## 220 2.034653
## 221 2.059102
## 222 2.124875
## 223 1.961029
## 224 2.071815
## 225 1.995713
## 226 2.116482
## 227 1.940377
## 228 2.007999
## 229 2.073191
## 230 2.040412
## 231 2.024606
## 232 1.987063
## 233 2.010875
## 234 2.024049
## 235 1.950578
## 236 2.004324
## 237 2.048674
## 238 2.031659
## 239 2.007426
## 240 2.059055
## 241 2.016101
## 242 1.931302
## 243 2.029270
## 244 1.975436
## 245 2.088698
## 246 2.037077
## 247 2.010427
## 248 1.970136
## 249 1.975429
## 250 1.978255
## 251 2.031361
## 252 2.098680
## 253 1.935349
## 254 2.020143
## 255 2.012662
## 256 1.949487
## 257 1.833694
## 258 1.964397
## 259 1.989521
## 260 2.034588
## 261 1.981868
## 262 2.124144
## 263 2.018369
## 264 1.884384
## 265 1.980487
## 266 1.988142
## 267 2.093288
## 268 1.878200
## 269 1.977295
## 270 1.967987
## 271 2.022501
## 272 1.891477
## 273 2.013864
## 274 1.907055
## 275 1.953401
## 276 1.938692
## 277 2.013092
## 278 1.994753
## 279 1.901902
## 280 1.989799
## 281 1.946057
## 282 1.992655
## 283 2.191434
## 284 2.037295
## 285 2.118522
## 286 1.963641
## 287 1.913415
## 288 1.964788
## 289 2.116387
## 290 1.998107
## 291 1.935369
## 292 2.038261
## 293 2.064849
## 294 2.175006
## 295 2.041414
## 296 1.964980
## 297 1.999206
## 298 2.037626
## 299 2.068411
## 300 2.045083
## 301 1.984966
## 302 2.073331
## 303 1.984694
## 304 2.428503
## 305 2.467283
## 306 1.484947
## 307 2.628259
## 308 2.441499
## 309 1.266452
## 310 2.520937
## 311 1.356217
## 312 1.330515
## 313 1.227540
## 314 1.651448
## 315 2.487183
## 316 2.432468
## 317 2.495821
## 318 2.545252
## 319 1.438225
## 320 3.958293
## 321 2.573778
## 322 2.503836
## 323 2.496813
## 324 2.613186
## 325 2.483875
## 326 1.193953
## 327 1.176907
## 328 2.191723
## 329 2.424384
## 330 2.521844
## 331 2.532945
## 332 2.552927
## 333 2.482091
## 334 2.604573
## 335 2.521811
## 336 2.513247
## 337 2.562089
## 338 2.529070
## 339 2.571434
## 340 2.566226
## 341 2.580555
## 342 2.564732
## 343 2.461038
## 344 2.617011
## 345 2.618553
## 346 2.433620
## 347 2.643146
## 348 2.602245
## 349 2.634059
## 350 2.517544
## 351 2.522118
## 352 2.596053
## 353 2.523319
## 354 2.554009
## 355 2.497908
## 356 2.489962
## 357 2.526359
## 358 2.666859
## 359 2.592212
## 360 2.464549
## 361 2.510704
## 362 2.585931
## 363 2.588038
## 364 2.569089
## 365 2.412895
## 366 2.725195
## 367 2.548240
## 368 2.637522
## 369 2.554894
## 370 2.489182
## 371 2.626007
## 372 2.620306
## 373 2.499348
## 374 2.536720
## 375 2.636109
## 376 2.539037
## 377 2.545811
## 378 2.530222
## 379 2.639541
## 380 2.634990
## 381 2.630686
## 382 2.592456
## 383 2.713891
## 384 2.535931
## 385 2.571295
## 386 2.481119
## 387 2.573297
## 388 2.768981
## 389 2.552295
## 390 2.557531
## 391 2.511359
## 392 2.545022
## 393 2.574046
## 394 2.511111
## 395 2.580591
## 396 2.683785
## 397 2.556316
## 398 2.541367
## 399 2.480745
## 400 2.434755
## 401 2.536563
## 402 2.782917
## 403 2.558605
## 404 2.692531
## 405 2.527517
## 406 2.592625
## 407 2.561296
## 408 2.432452
## 409 2.596429
## 410 2.491955
## 411 2.537090
## 412 2.428173
## 413 2.595577
## 414 2.511041
## 415 2.415213
## 416 2.676048
## 417 2.436148
## 418 2.550238
## 419 2.568701
## 420 2.585552
## 421 2.702124
## 422 2.568922
## 423 2.630112
## 424 2.513170
## 425 2.566208
## 426 2.531783
## 427 2.716950
## 428 2.449182
## 429 2.490702
## 430 2.499390
## 431 2.659461
## 432 2.696999
## 433 2.486327
## 434 2.751133
## 435 2.543737
## 436 2.458533
## 437 2.454571
## 438 2.462556
## 439 2.520118
## 440 2.497254
## 441 2.530238
## 442 2.625102
## 443 2.430602
## 444 2.576912
## 445 2.591835
## 446 2.486402
## 447 2.599078
## 448 2.431196
## 449 2.586003
## 450 2.617365
## 451 2.642217
## 452 2.647948
## 453 2.409836
## 454 2.601363
## 455 2.596131
## 456 2.494653
## 457 2.475953
## 458 2.510714
## 459 2.496369
## 460 2.402256
## 461 2.506077
## 462 2.514736
## 463 2.489406
## 464 2.528682
## 465 2.518221
## 466 2.687515
## 467 2.543948
## 468 2.415526
## 469 2.489688
## 470 2.443233
## 471 2.596443
## 472 2.569538
## 473 2.592350
## 474 2.630227
## 475 2.606971
## 476 2.563332
## 477 2.583912
## 478 2.702494
## 479 2.737811
## 480 2.626575
## 481 2.542442
## 482 2.588752
## 483 2.497402
## 484 2.540525
## 485 2.483411
## 486 2.620975
## 487 2.742715
## 488 2.440184
## 489 2.547879
## 490 2.338789
## 491 2.614354
## 492 2.521597
## 493 2.575491
## 494 2.513956
## 495 2.539307
## 496 2.492808
## 497 2.508954
## 498 2.500726
## 499 2.493727
## 500 2.795678
## 501 2.645321
## 502 2.585398
## 503 2.665253
## 504 2.559871
## 505 2.590067
## 506 2.533042
## 507 2.645450
## 508 2.680018
## 509 2.383111
## 510 2.553383
## 511 2.528032
## 512 2.617265
## 513 2.572867
## 514 2.552048
## 515 2.649133
## 516 2.518466
## 517 2.490822
## 518 2.585277
## 519 2.627326
## 520 2.461537
## 521 2.554136
## 522 2.518086
## 523 2.673662
## 524 2.486331
## 525 2.571790
## 526 2.511931
## 527 2.504615
## 528 2.561023
## 529 2.495055
## 530 2.577001
## 531 2.629534
## 532 2.517455
## 533 2.579493
## 534 2.411135
## 535 2.469142
## 536 2.554760
## 537 2.569947
## 538 2.545717
## 539 2.494875
## 540 2.520458
## 541 2.595579
## 542 2.535848
## 543 2.637295
## 544 2.583085
## 545 2.633520
## 546 2.596876
## 547 2.374014
## 548 2.640739
## 549 2.551324
## 550 2.543713
## 551 2.485095
## 552 2.611989
## 553 2.584816
## 554 2.545236
## 555 2.625202
## 556 2.460264
## 557 2.583265
## 558 2.616475
## 559 2.369880
## 560 2.530388
## 561 2.468212
## 562 2.505029
## 563 2.584160
## 564 2.559310
## 565 2.560657
## 566 2.588410
## 567 2.522927
## 568 2.549805
## 569 2.601262
## 570 2.419453
## 571 2.530546
## 572 2.472160
## 573 2.553297
## 574 2.478674
## 575 2.682588
## 576 2.394791
## 577 2.680753
## 578 2.630481
## 579 2.638941
## 580 2.553685
## 581 2.499343
## 582 2.587589
## 583 2.559265
## 584 2.363247
## 585 2.730654
## 586 2.398941
## 587 2.499837
## 588 2.607393
## 589 2.412933
## 590 2.581457
## 591 2.461373
## 592 2.544545
## 593 2.704661
## 594 2.495810
## 595 2.448338
## 596 2.349370
## 597 2.476047
## 598 2.551054
## 599 2.731237
## 600 2.508042
## 601 2.650888
## 602 2.461713
## 603 2.542168
## 604 2.497323
## 605 2.492921
## 606 2.600913
## 607 2.475344
## 608 2.724788
## 609 2.482292
## 610 2.468376
## 611 2.493166
## 612 2.497782
## 613 2.597521
## 614 2.514542
## 615 2.484145
## 616 2.548649
## 617 2.564210
## 618 2.581785
## 619 2.565860
## 620 2.689157
## 621 2.555166
## 622 2.566169
## 623 2.632502
## 624 2.560374
## 625 2.634459
## 626 2.656622
## 627 2.619652
## 628 2.571939
## 629 2.140158
## 630 2.161094
## 631 1.273599
## 632 2.311206
## 633 2.217782
## 634 1.180507
## 635 2.160592
## 636 1.215045
## 637 1.168098
## 638 1.184790
## 639 1.584217
## 640 2.093207
## 641 2.083396
## 642 2.155245
## 643 2.231620
## 644 1.172364
## 645 2.683018
## 646 2.198944
## 647 2.163242
## 648 2.326415
## 649 2.237784
## 650 2.190490
## 651 1.513740
## 652 1.632922
## 653 1.809163
## 654 2.109545
## 655 2.058444
## 656 2.128805
## 657 2.106327
## 658 2.001390
## 659 2.153629
## 660 2.055380
## 661 2.120588
## 662 1.956496
## 663 2.097987
## 664 2.026100
## 665 2.108134
## 666 2.189181
## 667 2.117661
## 668 2.004098
## 669 2.017742
## 670 2.094492
## 671 1.974951
## 672 2.080925
## 673 2.116347
## 674 2.096053
## 675 2.058166
## 676 1.994869
## 677 1.992457
## 678 2.009882
## 679 2.043975
## 680 2.074250
## 681 2.004047
## 682 2.039384
## 683 2.053544
## 684 2.148223
## 685 2.132181
## 686 2.043379
## 687 2.079544
## 688 1.959353
## 689 2.062883
## 690 1.984617
## 691 2.144202
## 692 2.136313
## 693 2.010840
## 694 2.001035
## 695 2.094812
## 696 2.125580
## 697 2.152643
## 698 2.051323
## 699 2.087526
## 700 2.126270
## 701 2.094501
## 702 2.046526
## 703 2.082257
## 704 2.027585
## 705 2.018020
## 706 2.063798
## 707 2.105169
## 708 2.034495
## 709 2.036700
## 710 1.973873
## 711 2.099295
## 712 2.075092
## 713 2.237424
## 714 2.158990
## 715 2.140423
## 716 2.035073
## 717 2.026759
## 718 2.116831
## 719 2.058611
## 720 2.051224
## 721 2.103557
## 722 2.204428
## 723 2.074486
## 724 2.124220
## 725 1.952977
## 726 2.030109
## 727 2.160609
## 728 2.140546
## 729 2.161691
## 730 2.020068
## 731 2.119320
## 732 2.209663
## 733 2.025725
## 734 2.086865
## 735 2.031086
## 736 1.996457
## 737 2.027883
## 738 2.040140
## 739 2.000743
## 740 1.987152
## 741 2.118928
## 742 2.034942
## 743 2.079291
## 744 2.079692
## 745 2.052837
## 746 2.138795
## 747 2.097013
## 748 2.059274
## 749 2.063625
## 750 2.086914
## 751 2.130523
## 752 2.099417
## 753 2.051342
## 754 2.048008
## 755 1.952570
## 756 2.152947
## 757 2.136326
## 758 1.998388
## 759 2.083767
## 760 2.102628
## 761 2.080830
## 762 2.003065
## 763 2.082593
## 764 2.121819
## 765 2.011999
## 766 2.156295
## 767 1.922607
## 768 2.131392
## 769 2.118014
## 770 2.122845
## 771 2.120259
## 772 1.984920
## 773 1.995102
## 774 2.124407
## 775 2.138020
## 776 2.123703
## 777 2.024604
## 778 1.954508
## 779 2.149775
## 780 2.120959
## 781 2.019074
## 782 2.042520
## 783 2.011214
## 784 2.007158
## 785 2.072492
## 786 1.997368
## 787 2.041860
## 788 1.987650
## 789 2.096780
## 790 2.080911
## 791 2.135164
## 792 2.139845
## 793 2.017740
## 794 2.050348
## 795 1.929369
## 796 2.116272
## 797 2.042323
## 798 2.033835
## 799 2.080483
## 800 2.070307
## 801 2.114492
## 802 2.109327
## 803 2.151916
## 804 2.139388
## 805 2.111744
## 806 1.934807
## 807 2.110259
## 808 2.118225
## 809 1.982764
## 810 2.021326
## 811 2.102993
## 812 2.161909
## 813 2.001453
## 814 2.043789
## 815 1.958304
## 816 2.151959
## 817 2.011894
## 818 2.054906
## 819 2.105352
## 820 2.009032
## 821 2.166549
## 822 2.126462
## 823 2.065118
## 824 2.012789
## 825 2.132537
## 826 2.079599
## 827 2.129852
## 828 2.044144
## 829 2.109267
## 830 2.063037
## 831 2.033091
## 832 2.054775
## 833 2.055604
## 834 2.104275
## 835 2.068880
## 836 2.117076
## 837 2.094117
## 838 2.085858
## 839 2.159849
## 840 2.047203
## 841 2.093090
## 842 1.972063
## 843 2.063294
## 844 2.098411
## 845 2.133772
## 846 2.100061
## 847 2.003267
## 848 1.979587
## 849 1.967856
## 850 2.075073
## 851 2.049968
## 852 2.047140
## 853 2.057309
## 854 1.984074
## 855 2.058768
## 856 2.143103
## 857 2.033499
## 858 2.128031
## 859 1.988740
## 860 1.990932
## 861 2.200287
## 862 2.103233
## 863 2.116960
## 864 2.096530
## 865 2.063334
## 866 2.087188
## 867 2.068500
## 868 2.059579
## 869 2.162446
## 870 2.036963
## 871 2.057512
## 872 2.003827
## 873 2.069747
## 874 2.108603
## 875 2.119820
## 876 2.054354
## 877 2.094425
## 878 1.961454
## 879 2.041597
## 880 2.087686
## 881 2.052192
## 882 2.065552
## 883 2.057895
## 884 1.921396
## 885 2.001025
## 886 1.986720
## 887 2.053027
## 888 2.082929
## 889 2.033056
## 890 2.041758
## 891 2.132878
## 892 2.109659
## 893 2.147723
## 894 2.117232
## 895 2.112056
## 896 2.056692
## 897 2.022522
## 898 2.114222
## 899 1.985420
## 900 2.160718
## 901 2.055353
## 902 2.141800
## 903 2.199766
## 904 2.202712
## 905 2.171786
## 906 2.102622
## 907 2.101655
## 908 2.090374
## 909 1.981467
## 910 2.086609
## 911 1.922598
## 912 2.035327
## 913 2.072840
## 914 1.926529
## 915 2.025484
## 916 2.052837
## 917 2.088815
## 918 2.120195
## 919 1.953715
## 920 2.025463
## 921 1.891888
## 922 2.060415
## 923 2.132616
## 924 2.137157
## 925 2.004492
## 926 2.053331
## 927 1.934652
## 928 2.002595
## 929 2.069764
## 930 1.953618
## 931 2.013820
## 932 1.998012
## 933 2.038309
## 934 2.001905
## 935 2.104656
## 936 2.036542
## 937 2.113036
## 938 2.070706
## 939 2.012276
## 940 1.959332
## 941 2.080930
## 942 2.130407
## 943 2.157330
## 944 2.092434
## 945 2.041466
## 946 1.987416
## 947 2.025688
## 948 2.101839
## 949 2.088773
## 950 2.146136
## 951 2.093944
## 952 2.125968
## 953 2.122707
## 954 1.940596
## 955 1.829106
## 956 1.511929
## 957 1.947638
## 958 1.885730
## 959 1.587666
## 960 1.736776
## 961 1.464111
## 962 1.493073
## 963 1.376340
## 964 1.652360
## 965 1.972096
## 966 1.829364
## 967 1.847455
## 968 1.823430
## 969 1.430790
## 970 2.156096
## 971 1.887226
## 972 1.876439
## 973 1.983400
## 974 1.910087
## 975 1.842566# We can access the estimated deviation between each series average Dim1 and the overall average:
ranef(md_final1)## $Source
## (Intercept) RegisterFiction
## Access -0.76244356309470196 1.658131412388347048
## Achievers -0.27292168586377225 -1.746777523614002181
## BBC -1.48451072245553073 0.013613910897331827
## Book1 1.56858377181185560 0.808150163526446752
## Book10 -1.17679073167432491 -0.606294441731024314
## Book100 -4.08057225803153933 -2.102351941203952634
## Book101 -0.04599447611808670 -0.023696817514062628
## Book102 -1.47092730944075067 -0.757836570173723967
## Book103 2.19972141274745159 1.133318635173096389
## Book104 2.29814210269453323 1.184025966272949315
## Book105 1.29592733002658322 0.667674817564663847
## Book106 -3.09785005023388571 -1.596043558314470756
## Book107 4.52201865598057129 2.329789573227002197
## Book108 -0.63054392914807389 -0.324862585351766708
## Book109 -3.49936336126617986 -1.802907261611608858
## Book11 -3.37502043363838800 -1.738844532478764915
## Book110 -1.49417949677374717 -0.769816331365440920
## Book111 -0.48989333975532773 -0.252397984569531553
## Book112 -2.84457841343940521 -1.465555459195834187
## Book113 -1.10070028342448234 -0.567091876142269302
## Book114 -3.68352499533869393 -1.897789191009034315
## Book115 4.66096903394114559 2.401378207948874799
## Book116 -0.37975076958218851 -0.195651422673235820
## Book117 -3.63227918597451982 -1.871386833696702112
## Book118 3.20314549072983690 1.650292830163751612
## Book119 1.63438913822902987 0.842053751327516586
## Book12 -2.30167216717778222 -1.185844691061936462
## Book120 -0.51048745006055729 -0.263008277695048087
## Book121 2.53380098585006452 1.305439797259239976
## Book122 -6.01759201207765315 -3.100324034959673902
## Book123 -0.59616852758626504 -0.307152032117256113
## Book124 -0.62649694700669833 -0.322777539377101075
## Book125 1.38709218808705370 0.714643870970294470
## Book126 -1.98982704921284870 -1.025178944286307070
## Book127 -1.44682501639166605 -0.745418825951825625
## Book128 1.58625639205053015 0.817255275534157555
## Book129 2.61045418013731867 1.344932295276424661
## Book13 -4.24757612462301193 -2.188394015921805824
## Book130 -0.24074052502189336 -0.124031944075998379
## Book131 0.28764208414656556 0.148196099894380873
## Book132 -1.38522228527515323 -0.713680478201370372
## Book133 -1.46180363964273674 -0.753135963568117073
## Book134 3.28645585090645653 1.693215167121510323
## Book135 1.26460512505072198 0.651537301973762251
## Book136 0.73354049221460527 0.377927452387036178
## Book137 -0.18111339169628918 -0.093311444212583597
## Book138 -1.41726288944770662 -0.730188120296650811
## Book139 0.59782291063915793 0.308004386934866703
## Book14 -2.45283533148215094 -1.263725476358243505
## Book140 -0.14506172534219311 -0.074737262467856447
## Book141 0.59581139288841412 0.306968032722607143
## Book142 0.85556059577308585 0.440793439155723221
## Book143 1.40360339629107189 0.723150611795926990
## Book144 2.95272350408224460 1.521272899512483123
## Book145 0.99684577112291228 0.513584985016825701
## Book146 -2.26049591988726961 -1.164630273585976150
## Book147 1.46440391497127642 0.754475651616493215
## Book148 -0.09125362550981052 -0.047014787289907572
## Book149 0.65692321788718644 0.338453460695070651
## Book15 -3.07307725126788700 -1.583280362688485710
## Book150 0.76468309739129081 0.393972436351844202
## Book151 1.35201036064849545 0.696569360006478289
## Book152 1.67603234499739395 0.863508751031322741
## Book153 0.75421841575944781 0.388580926938064342
## Book154 1.05227026914785649 0.542140244829626861
## Book155 3.63999206891953175 1.875360588701238118
## Book156 1.83593603286153928 0.945892742130901798
## Book157 1.26110891562681160 0.649736019652453312
## Book158 3.06696094967654043 1.580129182483735573
## Book159 -4.75286219961775647 -2.448722492776416182
## Book16 2.78989832615154265 1.437383727295098224
## Book160 0.87577155984007793 0.451206331478904654
## Book161 0.29567618182959371 0.152335347968346912
## Book162 0.62681031822576283 0.322938991386538865
## Book163 2.68561070943145941 1.383653696409487877
## Book164 -2.73780034617078316 -1.410542323095044681
## Book165 -0.55178343055051871 -0.284284382921738410
## Book166 0.08469431108050204 0.043635362407457495
## Book167 5.73246143581028988 2.953421889229392416
## Book168 0.70593001642166486 0.363702257068756363
## Book169 -0.41604190233518162 -0.214348979919422644
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## Dogo -0.661306362646309265
## Ducksters -1.699981363987139726
## EIM 1.367131560834593529
## Encyclopedia -0.500016960547408296
## Factmonster -3.555233125488755697
## GreenLine 0.964283132993086434
## History -1.465873743077808822
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## JTT -3.052325585958935328
## NGL -0.852107547059569703
## POC 0.579985832516267785
## Quatr 0.403676632830194004
## Revision 0.179397247079479738
## Science 0.116860483347946104
## Science_Tech -0.351607727246369628
## Solutions 2.489426104726500721
## Spoken.BNC2014 -0.000000000002809848
## Teen 2.755004335184580544
## TeenVogue 3.358461159749563052
## TweenTribute 1.959133280354183659
## WhyFiles 0.806464364279367407
## World -0.414189947417665905
##
## with conditional variances for "Source"# Diagnostic plots (in new window)
residplot(md_final1)
## Plot predicted vs. observed values
dimensions_ref[, "predicted"] <- predict(md_final1)
dimensions_ref %>%
ggplot(aes(x = Corpus, y = Dim1nopunct)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
geom_point(aes(y = predicted), shape = "triangle filled", fill = "red", position = position_jitter(width = 0.2, height = 0)) +
facet_wrap(vars(Register))
## Compare means
comparisons <- emmeans(md_final1, "Corpus", by = "Register")## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.comparisons## Register = Conversation:
## Corpus emmean SE df asymp.LCL asymp.UCL
## Textbook 14.65 1.206 Inf 12.290 17.02
## Reference 30.66 3.517 Inf 23.769 37.56
##
## Register = Fiction:
## Corpus emmean SE df asymp.LCL asymp.UCL
## Textbook 3.91 1.681 Inf 0.612 7.20
## Reference 4.43 0.328 Inf 3.789 5.08
##
## Register = Informative:
## Corpus emmean SE df asymp.LCL asymp.UCL
## Textbook -6.07 1.599 Inf -9.202 -2.93
## Reference -12.20 1.220 Inf -14.596 -9.81
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95pairs(comparisons)## Register = Conversation:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -16.009 3.72 Inf -4.305 <.0001
##
## Register = Fiction:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -0.525 1.71 Inf -0.306 0.7593
##
## Register = Informative:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference 6.137 2.01 Inf 3.051 0.0023
##
## Degrees-of-freedom method: asymptoticconfint(comparisons)## Register = Conversation:
## Corpus emmean SE df asymp.LCL asymp.UCL
## Textbook 14.65 1.206 Inf 12.290 17.02
## Reference 30.66 3.517 Inf 23.769 37.56
##
## Register = Fiction:
## Corpus emmean SE df asymp.LCL asymp.UCL
## Textbook 3.91 1.681 Inf 0.612 7.20
## Reference 4.43 0.328 Inf 3.789 5.08
##
## Register = Informative:
## Corpus emmean SE df asymp.LCL asymp.UCL
## Textbook -6.07 1.599 Inf -9.202 -2.93
## Reference -12.20 1.220 Inf -14.596 -9.81
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95# This is a warning that the degrees of freedom have been calculated according to the naive 'asymptotic' method (i.e. are assumed to be infinite), because we have a very large number of observations and so a more complex estimation method like Kenward-Roger might take a lot of computation.Dimension 2: Narrative vs. Non-narrative
# Rainplots for all textbook registers and the three ref. corpora on Dimension 2
Dimensions[Dimensions$Dim2>15,] # Remove one poetry outlier with a very high Dim 2 score## Corpus Filename Level Dim1
## 1674 Textbook.English Solutions_Elementary_Poetry_0001.txt A 17.4036
## Dim2 Dim3 Dim4 Dim5 Dim6 TextType Register
## 1674 16.6553 -2.2428 -0.7473 -3.9201 -2.6293 Imaginative narrative Poetry
## Series Country
## 1674 Solutions SpainDimensions2 <- Dimensions[Dimensions$Dim2<15,]
colours <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
colours2 <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
colours <- c(colours, colours2[c(2:4)]) # Nine colours range
#scales::show_col(colours)
p2 <- ggplot(Dimensions2,aes(x=Register,y=Dim2, fill = Register, colour = Register))+ #Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
#note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim2), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 2 (Biber 1988)')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
annotate(geom = "text", x = 8.3, y = -10.5, label = "Reference Corpora", size = 5) +
annotate(geom = "segment", x = 7, xend = 9.5, y = -9.5, yend = -9.5) +
annotate(geom = "text", x = 3.8, y = -10.5, label = "Textbook Corpus", size = 5) +
annotate(geom = "segment", x = 1, xend = 6.5, y = -9.5, yend = -9.5) +
ggtitle("Dimension 2: Narrative vs. Non-narrative Concerns")
p2 + scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -15, to = 20, by = 5))
#ggsave(here("plots", "Dim2.svg"), width = 13, height = 8)# Rainplots comparing the three textbook and reference corpora on Dimension 2
ggplot(dimensions_ref,aes(x=Subcorpus,y=Dim2, fill = Subcorpus, colour = Subcorpus))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Subcorpus)+0.25, y = Dim2), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 2 (Biber 1988)')+
theme_cowplot()+
theme(axis.title.x=element_blank())+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours3Reg)+
scale_fill_manual(values = colours3Reg) +
annotate(geom = "text", x = 1.5, y = -11, label = "Conversation", size = 5) +
annotate(geom = "segment", x = 0.7, xend = 2.5, y = -10, yend = -10) +
annotate(geom = "text", x = 3.5, y = -11, label = "Fiction", size = 5) +
annotate(geom = "segment", x = 2.7, xend = 4.5, y = -10, yend = -10) +
annotate(geom = "text", x = 5.7, y = -11, label = "Informative", size = 5) +
annotate(geom = "segment", x = 4.7, xend = 6.5, y = -10, yend = -10) +
scale_x_discrete(labels=rep(c("Reference", "Textbook"), 3))+
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -14, to = 14, by = 2))
#ggsave(here("plots", "Dim2_3RegComparison.svg"), width = 13, height = 8)# Mixed effect model
# Check distribution of the outcome variable.
ggplot(dimensions_ref, aes(x = Dim2)) +
geom_histogram(bins = 20) +
facet_grid(rows = vars(Register), cols = vars(Corpus), scales = "free_y")
md0 <- lmer(Dim2 ~ 1 + (Register|Source), dimensions_ref, REML = FALSE)
md_corpus <- update(md0, .~. + Corpus)
md_register <- update(md0, . ~ . + Register)
md_both <- update(md_corpus, .~. + Register)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0619279 (tol = 0.002, component 1)md_interaction <- update(md_both, . ~ . + Corpus:Register)
anova(md0, md_corpus, md_both, md_interaction)## Data: dimensions_ref
## Models:
## md0: Dim2 ~ 1 + (Register | Source)
## md_corpus: Dim2 ~ (Register | Source) + Corpus
## md_both: Dim2 ~ (Register | Source) + Corpus + Register
## md_interaction: Dim2 ~ (Register | Source) + Corpus + Register + Corpus:Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 8 21440 21492 -10712 21424
## md_corpus 9 21433 21492 -10707 21415 9.2772 1 0.00232
## md_both 11 21345 21417 -10662 21323 91.7409 2 < 0.0000000000000002
## md_interaction 13 21343 21428 -10658 21317 6.1982 2 0.04509
##
## md0
## md_corpus **
## md_both ***
## md_interaction *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(md0, md_register)## Data: dimensions_ref
## Models:
## md0: Dim2 ~ 1 + (Register | Source)
## md_register: Dim2 ~ (Register | Source) + Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 8 21440 21492 -10712 21424
## md_register 10 21357 21422 -10668 21337 87.226 2 < 0.00000000000000022
##
## md0
## md_register ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1md_final2 <- lmer(Dim2 ~ 1 + Corpus + Register + Corpus:Register + (Register|Source), dimensions_ref)
tab_model(md_final2)| Dim 2 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -3.10 | -3.47 – -2.74 | <0.001 |
| Corpus [Reference] | 1.15 | 0.14 – 2.17 | 0.026 |
| Register [Fiction] | 5.75 | 4.51 – 6.98 | <0.001 |
| Register [Informative] | 1.37 | 0.68 – 2.06 | <0.001 |
|
Corpus [Reference] * Register [Fiction] |
1.10 | -0.47 – 2.68 | 0.169 |
|
Corpus [Reference] * Register [Informative] |
-1.15 | -2.45 – 0.16 | 0.084 |
| Random Effects | |||
| σ2 | 3.67 | ||
| τ00 Source | 0.23 | ||
| τ11 Source.RegisterFiction | 3.23 | ||
| τ11 Source.RegisterInformative | 0.92 | ||
| ρ01 | -0.58 | ||
| 0.10 | |||
| ICC | 0.25 | ||
| N Source | 325 | ||
| Observations | 5033 | ||
| Marginal R2 / Conditional R2 | 0.649 / 0.738 | ||
# Diagnostic plots (in new window)
residplot(md_final2) # I've seen worse, but I've also seen better...
# This function can be used to estimate mean and sd of all the terms of the model
library(merTools)
(randomSims <- REsim(md_final2, n.sims = 500))## groupFctr groupID term mean median
## 1 Source Access (Intercept) -0.882428581 -0.8785489075
## 2 Source Achievers (Intercept) 0.199800508 0.2060298318
## 3 Source BBC (Intercept) 0.190793331 0.1360883224
## 4 Source Book1 (Intercept) -0.041592488 -0.0331700008
## 5 Source Book10 (Intercept) 0.109794092 0.1265650193
## 6 Source Book100 (Intercept) -0.017174211 -0.0197542967
## 7 Source Book101 (Intercept) 0.130652034 0.1285118054
## 8 Source Book102 (Intercept) 0.024651742 0.0542703656
## 9 Source Book103 (Intercept) -0.133811965 -0.1560515244
## 10 Source Book104 (Intercept) 0.246470638 0.2426626056
## 11 Source Book105 (Intercept) 0.129970743 0.1470971618
## 12 Source Book106 (Intercept) 0.148008601 0.1718435032
## 13 Source Book107 (Intercept) -0.076117123 -0.0937249292
## 14 Source Book108 (Intercept) -0.193536459 -0.1846967447
## 15 Source Book109 (Intercept) -0.043113891 -0.0531284120
## 16 Source Book11 (Intercept) 0.067110519 0.0898802046
## 17 Source Book110 (Intercept) 0.092638800 0.1396305004
## 18 Source Book111 (Intercept) 0.372847043 0.3917447078
## 19 Source Book112 (Intercept) -0.090328551 -0.0943664268
## 20 Source Book113 (Intercept) -0.085031239 -0.0961611084
## 21 Source Book114 (Intercept) -0.062212418 -0.0397267175
## 22 Source Book115 (Intercept) 0.152008009 0.1876498439
## 23 Source Book116 (Intercept) 0.215168596 0.2134949816
## 24 Source Book117 (Intercept) -0.156615947 -0.1447465151
## 25 Source Book118 (Intercept) 0.037327567 0.0351978134
## 26 Source Book119 (Intercept) -0.031472668 -0.0493354182
## 27 Source Book12 (Intercept) -0.096096913 -0.0824603553
## 28 Source Book120 (Intercept) -0.033383486 -0.0354378961
## 29 Source Book121 (Intercept) -0.042621947 -0.0474167904
## 30 Source Book122 (Intercept) 0.058385879 0.0017634125
## 31 Source Book123 (Intercept) -0.069599783 -0.0835531557
## 32 Source Book124 (Intercept) -0.002821799 -0.0013216188
## 33 Source Book125 (Intercept) 0.148128256 0.1873460844
## 34 Source Book126 (Intercept) -0.107278578 -0.1216251887
## 35 Source Book127 (Intercept) -0.017169757 -0.0257191962
## 36 Source Book128 (Intercept) -0.088128200 -0.0692394490
## 37 Source Book129 (Intercept) -0.129304598 -0.1232625889
## 38 Source Book13 (Intercept) 0.194875342 0.1990671838
## 39 Source Book130 (Intercept) 0.016272673 -0.0104903740
## 40 Source Book131 (Intercept) -0.099533926 -0.1207582090
## 41 Source Book132 (Intercept) -0.132233515 -0.1459400826
## 42 Source Book133 (Intercept) -0.046563028 -0.0373215541
## 43 Source Book134 (Intercept) -0.099215640 -0.0824087389
## 44 Source Book135 (Intercept) -0.081025062 -0.0870972728
## 45 Source Book136 (Intercept) -0.112771162 -0.0973638989
## 46 Source Book137 (Intercept) -0.071945809 -0.0610046277
## 47 Source Book138 (Intercept) -0.055669729 -0.0736183750
## 48 Source Book139 (Intercept) -0.123488748 -0.1338279538
## 49 Source Book14 (Intercept) 0.004010921 -0.0204996159
## 50 Source Book140 (Intercept) 0.017504624 -0.0159453144
## 51 Source Book141 (Intercept) -0.095068984 -0.0875634399
## 52 Source Book142 (Intercept) -0.006421892 0.0174458255
## 53 Source Book143 (Intercept) 0.004021176 0.0390287966
## 54 Source Book144 (Intercept) -0.087395584 -0.1083553116
## 55 Source Book145 (Intercept) -0.237871345 -0.2443592294
## 56 Source Book146 (Intercept) -0.013322538 0.0013589155
## 57 Source Book147 (Intercept) -0.064006501 -0.0855909355
## 58 Source Book148 (Intercept) 0.064051030 0.0782960587
## 59 Source Book149 (Intercept) 0.121434877 0.1461299333
## 60 Source Book15 (Intercept) -0.111770395 -0.1021581697
## 61 Source Book150 (Intercept) -0.180595810 -0.2113761628
## 62 Source Book151 (Intercept) -0.047082545 -0.0262241911
## 63 Source Book152 (Intercept) 0.274105197 0.2658646740
## 64 Source Book153 (Intercept) -0.219300088 -0.2185463629
## 65 Source Book154 (Intercept) -0.079142064 -0.0879778008
## 66 Source Book155 (Intercept) -0.190776297 -0.2020267579
## 67 Source Book156 (Intercept) -0.138248227 -0.0960578534
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## 753 Source Book189 RegisterInformative 0.409554523 0.4576412055
## 754 Source Book19 RegisterInformative -0.914890147 -0.9224334637
## 755 Source Book190 RegisterInformative -0.243088934 -0.2737783253
## 756 Source Book191 RegisterInformative 0.235922762 0.1907899982
## 757 Source Book192 RegisterInformative 0.077845676 0.0764675570
## 758 Source Book193 RegisterInformative 0.077780722 0.0228099922
## 759 Source Book194 RegisterInformative 0.263071033 0.2535911739
## 760 Source Book195 RegisterInformative -0.006829959 0.0074747626
## 761 Source Book196 RegisterInformative 0.702566406 0.7258409679
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## 893 Source Book44 RegisterInformative 1.129952561 1.1653770520
## 894 Source Book45 RegisterInformative 0.152381595 0.1829385559
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## 903 Source Book53 RegisterInformative -0.466730838 -0.4810444294
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## 906 Source Book56 RegisterInformative 0.310505962 0.3290265985
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## 908 Source Book58 RegisterInformative 0.194896696 0.1905584407
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## 912 Source Book61 RegisterInformative 0.676624088 0.7135487554
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## 914 Source Book63 RegisterInformative 0.145697493 0.1584908469
## 915 Source Book64 RegisterInformative -0.112637122 -0.0980905489
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## 917 Source Book66 RegisterInformative 0.255465174 0.2358822232
## 918 Source Book67 RegisterInformative -0.447793735 -0.4027515076
## 919 Source Book68 RegisterInformative 0.541243759 0.5583677610
## 920 Source Book69 RegisterInformative -0.203073302 -0.2050965458
## 921 Source Book7 RegisterInformative 0.998471252 1.0064439584
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## 923 Source Book71 RegisterInformative 0.504683821 0.4851385611
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## 925 Source Book73 RegisterInformative 0.296264022 0.3199763364
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## 933 Source Book80 RegisterInformative 0.429510552 0.4234922429
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## 936 Source Book83 RegisterInformative -0.803634448 -0.8391838804
## 937 Source Book84 RegisterInformative -0.010944635 -0.0028401012
## 938 Source Book85 RegisterInformative 1.567658986 1.5441937065
## 939 Source Book86 RegisterInformative 0.538282064 0.5709297506
## 940 Source Book87 RegisterInformative -0.206840464 -0.1948625810
## 941 Source Book88 RegisterInformative -0.905505402 -0.8758085454
## 942 Source Book89 RegisterInformative 0.937973618 0.9648949075
## 943 Source Book9 RegisterInformative 0.262468957 0.2578274674
## 944 Source Book90 RegisterInformative 0.295783285 0.2740032766
## 945 Source Book91 RegisterInformative 0.006797191 -0.0005768066
## 946 Source Book92 RegisterInformative 1.434561639 1.4608151768
## 947 Source Book93 RegisterInformative 0.085658471 0.1089753314
## 948 Source Book94 RegisterInformative -0.642070521 -0.6140759832
## 949 Source Book95 RegisterInformative -0.958654281 -0.9939692681
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## 951 Source Book97 RegisterInformative 0.640106639 0.6577056537
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## 953 Source Book99 RegisterInformative -0.678022019 -0.6863984774
## 954 Source Dogo RegisterInformative 0.365083457 0.3904939352
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## 956 Source EIM RegisterInformative 0.614294912 0.5967644655
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## 958 Source Factmonster RegisterInformative 0.526057731 0.5633473112
## 959 Source GreenLine RegisterInformative 0.010093833 -0.0002796852
## 960 Source History RegisterInformative 0.735246245 0.7173316998
## 961 Source HT RegisterInformative -0.845386394 -0.8523977017
## 962 Source JTT RegisterInformative -0.340767522 -0.3471577264
## 963 Source NGL RegisterInformative 0.013177242 0.0085514574
## 964 Source POC RegisterInformative -0.598773532 -0.5796577028
## 965 Source Quatr RegisterInformative -0.058967670 -0.0518246899
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## 967 Source Science RegisterInformative -0.870202282 -0.8505099830
## 968 Source Science_Tech RegisterInformative -2.181147463 -2.1703089670
## 969 Source Solutions RegisterInformative 0.959757589 0.9895234679
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## 972 Source TeenVogue RegisterInformative 1.398969633 1.3797493190
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## 974 Source WhyFiles RegisterInformative 0.597273444 0.5796412744
## 975 Source World RegisterInformative 0.114004954 0.1117743599
## sd
## 1 0.4156689
## 2 0.4164900
## 3 0.7837332
## 4 0.5898412
## 5 0.6121647
## 6 0.6210976
## 7 0.6044885
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## 281 0.6108265
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## 283 0.6045749
## 284 0.6401561
## 285 0.6118915
## 286 0.5944019
## 287 0.6067542
## 288 0.6355580
## 289 0.6129178
## 290 0.6072284
## 291 0.5937540
## 292 0.5914546
## 293 0.5837916
## 294 0.6314292
## 295 0.6047005
## 296 0.6303064
## 297 0.6210911
## 298 0.5770784
## 299 0.5987870
## 300 0.6250179
## 301 0.5908241
## 302 0.6088438
## 303 0.5944566
## 304 0.8170083
## 305 0.7890567
## 306 0.5123380
## 307 0.8007828
## 308 0.8088418
## 309 0.4196986
## 310 0.8075859
## 311 0.4484977
## 312 0.4319364
## 313 0.4218969
## 314 0.6523811
## 315 0.8433477
## 316 0.7697181
## 317 0.7837213
## 318 0.7748318
## 319 0.5511056
## 320 1.0763088
## 321 0.8037587
## 322 0.7654706
## 323 0.8248317
## 324 0.7873524
## 325 0.7736780
## 326 0.6725325
## 327 0.7206089
## 328 1.1622588
## 329 1.0196066
## 330 1.0576260
## 331 1.0801773
## 332 1.0474261
## 333 1.1019777
## 334 1.0592771
## 335 1.0826461
## 336 1.1289990
## 337 1.0907851
## 338 1.0889242
## 339 1.1232974
## 340 1.0984392
## 341 1.0488933
## 342 1.1218000
## 343 1.0893996
## 344 1.0530222
## 345 1.0971568
## 346 1.0573368
## 347 1.0546267
## 348 1.1170840
## 349 1.0578803
## 350 1.0962772
## 351 1.0697000
## 352 0.9980265
## 353 1.0876392
## 354 1.0774233
## 355 1.0552049
## 356 1.0569342
## 357 1.0928147
## 358 1.0835351
## 359 1.0736905
## 360 1.0219953
## 361 1.0457845
## 362 1.0379435
## 363 1.0381199
## 364 1.0583913
## 365 1.0848521
## 366 1.0726768
## 367 1.0634018
## 368 1.0336098
## 369 1.1200776
## 370 1.0694120
## 371 1.1456401
## 372 1.0416730
## 373 1.0285948
## 374 1.0531293
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## 376 1.0726829
## 377 1.0471075
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## 379 1.0740231
## 380 1.1387166
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## 383 1.0711130
## 384 1.1150589
## 385 1.0841913
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## 388 1.1296417
## 389 1.0885897
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## 392 1.1134325
## 393 1.0772341
## 394 1.0452383
## 395 1.0917478
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## 397 1.0273247
## 398 1.0837611
## 399 1.0882876
## 400 1.0317132
## 401 1.0948291
## 402 1.0483982
## 403 1.0008973
## 404 1.0720294
## 405 1.1361216
## 406 1.0983355
## 407 1.1002336
## 408 1.0689328
## 409 1.1179261
## 410 1.1263933
## 411 1.1207903
## 412 1.0606797
## 413 1.1101005
## 414 1.0643913
## 415 1.0702589
## 416 1.1073153
## 417 0.9949185
## 418 1.1266085
## 419 1.0539243
## 420 1.0382547
## 421 1.1172343
## 422 1.0620984
## 423 1.0510363
## 424 1.0210428
## 425 1.0756427
## 426 1.0157063
## 427 1.0314591
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## 429 1.0571314
## 430 1.0868698
## 431 1.0368458
## 432 1.0353532
## 433 1.0586805
## 434 1.0837073
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## 436 1.0307247
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## 438 1.0826950
## 439 1.0056225
## 440 1.0484664
## 441 1.1007262
## 442 1.0594657
## 443 1.0798097
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## 445 1.0667750
## 446 1.0671810
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## 448 1.0911735
## 449 1.0799033
## 450 1.0183312
## 451 1.0537235
## 452 1.0817082
## 453 1.0906587
## 454 1.0553100
## 455 1.0636389
## 456 1.1102229
## 457 1.0710505
## 458 1.0302130
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## 460 1.0448970
## 461 1.0566510
## 462 0.9994065
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## 464 1.1106803
## 465 1.0330213
## 466 1.0798934
## 467 1.0634287
## 468 0.9937375
## 469 1.1503421
## 470 1.0239251
## 471 1.0635863
## 472 1.0565377
## 473 1.0480155
## 474 1.0751627
## 475 1.0248769
## 476 1.0813922
## 477 1.0872790
## 478 1.0219827
## 479 1.0717732
## 480 1.0100869
## 481 1.1369750
## 482 1.0647762
## 483 1.1383143
## 484 1.3745766
## 485 1.0961328
## 486 1.0863654
## 487 1.0715850
## 488 1.0974104
## 489 1.0408408
## 490 1.0982768
## 491 1.0463946
## 492 1.0720581
## 493 1.0844405
## 494 1.0047470
## 495 1.1142711
## 496 1.0494631
## 497 1.0616916
## 498 1.0906715
## 499 1.0460702
## 500 1.0813353
## 501 1.0665167
## 502 1.0590487
## 503 1.0838797
## 504 1.0623970
## 505 1.0576611
## 506 1.0851974
## 507 1.0926495
## 508 1.0374702
## 509 1.0671475
## 510 1.3255767
## 511 1.0656175
## 512 1.1004391
## 513 1.0579151
## 514 1.0498661
## 515 1.2401219
## 516 1.0695229
## 517 1.0244992
## 518 1.1352837
## 519 1.0992026
## 520 1.1212860
## 521 1.1068342
## 522 1.0376145
## 523 1.0599693
## 524 1.0629462
## 525 1.0883125
## 526 1.0126311
## 527 1.0003281
## 528 1.0899433
## 529 1.0835547
## 530 1.0607805
## 531 1.1539416
## 532 1.0897567
## 533 1.0937154
## 534 1.0786013
## 535 1.0774882
## 536 1.1153190
## 537 1.0254438
## 538 1.1108674
## 539 1.0586576
## 540 1.0606882
## 541 1.1227741
## 542 1.0109812
## 543 1.0708797
## 544 1.0812381
## 545 1.0596027
## 546 1.0395079
## 547 1.0615823
## 548 1.0305689
## 549 1.0473744
## 550 1.0592968
## 551 1.0933914
## 552 1.1270481
## 553 1.1059511
## 554 1.0204301
## 555 1.0865907
## 556 1.0910774
## 557 1.0113172
## 558 1.0156362
## 559 1.0670746
## 560 1.1194213
## 561 1.0719453
## 562 1.0526140
## 563 1.0646119
## 564 1.0808106
## 565 1.0753249
## 566 1.0432891
## 567 1.0842998
## 568 1.1097800
## 569 1.0450696
## 570 1.1195586
## 571 1.0820945
## 572 1.0521353
## 573 1.0901588
## 574 1.0406244
## 575 1.0796182
## 576 1.0846914
## 577 1.1091052
## 578 1.0062400
## 579 1.0762406
## 580 1.0728329
## 581 1.0882935
## 582 1.0401896
## 583 1.0878405
## 584 1.0703552
## 585 1.0744398
## 586 1.0371693
## 587 1.1051450
## 588 1.0860457
## 589 1.1092362
## 590 1.0770075
## 591 1.0528506
## 592 1.1479583
## 593 1.1111731
## 594 1.0641791
## 595 1.1336062
## 596 1.1063693
## 597 1.0533902
## 598 1.0301527
## 599 1.1316820
## 600 1.0704431
## 601 1.0667108
## 602 1.0940635
## 603 1.0617583
## 604 1.0468972
## 605 1.0301568
## 606 1.0820181
## 607 1.0464271
## 608 1.0991652
## 609 1.1111474
## 610 1.1238483
## 611 1.0848086
## 612 1.0498864
## 613 1.0627906
## 614 1.1495494
## 615 1.0803959
## 616 1.1029074
## 617 1.0680815
## 618 1.0311070
## 619 1.0922289
## 620 1.0144627
## 621 1.0925729
## 622 1.1282955
## 623 1.0011826
## 624 1.0207198
## 625 1.0736684
## 626 1.0144900
## 627 1.0574659
## 628 1.0947958
## 629 1.1537429
## 630 1.1085425
## 631 0.7418385
## 632 1.1318902
## 633 1.0846068
## 634 0.7505756
## 635 1.0560540
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## 637 0.7051467
## 638 0.6773258
## 639 0.9203595
## 640 1.1281905
## 641 1.0975842
## 642 1.1347436
## 643 1.0740695
## 644 0.7489783
## 645 1.4844937
## 646 1.1184737
## 647 1.0597482
## 648 1.1252655
## 649 1.1147219
## 650 1.1274399
## 651 0.4283157
## 652 0.3915272
## 653 0.5588784
## 654 0.7200516
## 655 0.7551518
## 656 0.7052890
## 657 0.6855785
## 658 0.7001970
## 659 0.7263001
## 660 0.7407848
## 661 0.7527776
## 662 0.7676135
## 663 0.7851809
## 664 0.7368320
## 665 0.7548075
## 666 0.7590378
## 667 0.7148398
## 668 0.7283613
## 669 0.7864078
## 670 0.7673906
## 671 0.7222830
## 672 0.7743647
## 673 0.7252847
## 674 0.7728669
## 675 0.7539985
## 676 0.7401885
## 677 0.7304461
## 678 0.7332767
## 679 0.7536081
## 680 0.7522116
## 681 0.7469639
## 682 0.7010930
## 683 0.7453302
## 684 0.7382419
## 685 0.7579890
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## 687 0.7256736
## 688 0.7459693
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## 690 0.7338955
## 691 0.7034957
## 692 0.7332019
## 693 0.7447322
## 694 0.7707842
## 695 0.7438279
## 696 0.8031153
## 697 0.7477535
## 698 0.7031233
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## 700 0.7649130
## 701 0.7194503
## 702 0.7401601
## 703 0.7229851
## 704 0.7832351
## 705 0.7256845
## 706 0.7299575
## 707 0.7286533
## 708 0.7402337
## 709 0.7744357
## 710 0.7373843
## 711 0.7754419
## 712 0.7383727
## 713 0.7483252
## 714 0.7299988
## 715 0.7214773
## 716 0.7302625
## 717 0.7767335
## 718 0.7636550
## 719 0.7395239
## 720 0.7324986
## 721 0.7814246
## 722 0.7131367
## 723 0.7405845
## 724 0.7716846
## 725 0.7314369
## 726 0.7633097
## 727 0.7663116
## 728 0.7061864
## 729 0.7288348
## 730 0.7635656
## 731 0.7508636
## 732 0.8003157
## 733 0.6912268
## 734 0.7552874
## 735 0.7801618
## 736 0.7032138
## 737 0.7417324
## 738 0.7521494
## 739 0.7627904
## 740 0.7608060
## 741 0.7283254
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## 743 0.7852458
## 744 0.7252050
## 745 0.7233660
## 746 0.7642843
## 747 0.7092617
## 748 0.7404947
## 749 0.7242168
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## 751 0.7232377
## 752 0.7171938
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## 754 0.7560017
## 755 0.7625589
## 756 0.7046678
## 757 0.7166976
## 758 0.6775339
## 759 0.7606577
## 760 0.7198687
## 761 0.7422171
## 762 0.7342001
## 763 0.6816254
## 764 0.7380909
## 765 0.7159844
## 766 0.7372510
## 767 0.7471325
## 768 0.7453982
## 769 0.7137533
## 770 0.7386424
## 771 0.7391711
## 772 0.7115376
## 773 0.7286510
## 774 0.7325219
## 775 0.7456991
## 776 0.7154346
## 777 0.7430996
## 778 0.7465847
## 779 0.7412245
## 780 0.7276010
## 781 0.7563428
## 782 0.7486346
## 783 0.7079338
## 784 0.7790349
## 785 0.7378496
## 786 0.7526025
## 787 0.7063730
## 788 0.7223443
## 789 0.7402876
## 790 0.7287865
## 791 0.7549479
## 792 0.7717874
## 793 0.7525019
## 794 0.7570750
## 795 0.7646610
## 796 0.7227695
## 797 0.7331847
## 798 0.7111210
## 799 0.7195320
## 800 0.7260303
## 801 0.7498212
## 802 0.7181282
## 803 0.7460001
## 804 0.7568831
## 805 0.7040144
## 806 0.7400329
## 807 0.7490139
## 808 0.7497499
## 809 0.7297840
## 810 0.7804970
## 811 0.7061162
## 812 0.7492727
## 813 0.7578967
## 814 0.7570769
## 815 0.7477121
## 816 0.7746079
## 817 0.7582579
## 818 0.7418013
## 819 0.7210385
## 820 0.7275879
## 821 0.7296830
## 822 0.7505676
## 823 0.7047353
## 824 0.7151293
## 825 0.7714896
## 826 0.7129467
## 827 0.7414991
## 828 0.7249714
## 829 0.7883587
## 830 0.7242997
## 831 0.7520808
## 832 0.7186139
## 833 0.7483110
## 834 0.7205428
## 835 0.7407106
## 836 0.7598665
## 837 0.7855574
## 838 0.7121192
## 839 0.7440774
## 840 0.7557104
## 841 0.7367868
## 842 0.7674043
## 843 0.7769044
## 844 0.7652271
## 845 0.7053182
## 846 0.7182617
## 847 0.7311512
## 848 0.7226690
## 849 0.7072845
## 850 0.7168487
## 851 0.7387421
## 852 0.7636658
## 853 0.7698211
## 854 0.7262551
## 855 0.7345581
## 856 0.7772502
## 857 0.7771878
## 858 0.7607255
## 859 0.7653086
## 860 0.7439234
## 861 0.7678074
## 862 0.7220626
## 863 0.7216483
## 864 0.7141620
## 865 0.7544002
## 866 0.7277274
## 867 0.7397052
## 868 0.7539319
## 869 0.7398581
## 870 0.7386427
## 871 0.7705670
## 872 0.7522593
## 873 0.7414441
## 874 0.6948297
## 875 0.7356047
## 876 0.7935104
## 877 0.7554346
## 878 0.7419777
## 879 0.6893367
## 880 0.7654247
## 881 0.7529001
## 882 0.6909348
## 883 0.7352001
## 884 0.7134873
## 885 0.7313394
## 886 0.7178730
## 887 0.7258425
## 888 0.7496946
## 889 0.7376079
## 890 0.7389575
## 891 0.6979122
## 892 0.7618618
## 893 0.7445492
## 894 0.7744710
## 895 0.7580115
## 896 0.7024774
## 897 0.7075978
## 898 0.7630501
## 899 0.7419265
## 900 0.7816151
## 901 0.7668001
## 902 0.7644076
## 903 0.7030022
## 904 0.7231372
## 905 0.7501219
## 906 0.7400955
## 907 0.7196520
## 908 0.7526148
## 909 0.7479391
## 910 0.7643542
## 911 0.7040428
## 912 0.7568539
## 913 0.7525163
## 914 0.8003050
## 915 0.7641240
## 916 0.7666921
## 917 0.7651778
## 918 0.7699912
## 919 0.7394165
## 920 0.7495449
## 921 0.7161281
## 922 0.7723713
## 923 0.7467721
## 924 0.7116218
## 925 0.7368474
## 926 0.7132191
## 927 0.6940617
## 928 0.7330442
## 929 0.7208235
## 930 0.7458842
## 931 0.7577433
## 932 0.7455403
## 933 0.7418804
## 934 0.7968792
## 935 0.7362157
## 936 0.7510551
## 937 0.7303143
## 938 0.7303777
## 939 0.7503829
## 940 0.7488281
## 941 0.7550064
## 942 0.7683137
## 943 0.7284277
## 944 0.7549093
## 945 0.7102605
## 946 0.7317904
## 947 0.7669386
## 948 0.7342083
## 949 0.7535312
## 950 0.7193403
## 951 0.7602047
## 952 0.7421264
## 953 0.7424823
## 954 0.5777185
## 955 0.6184890
## 956 0.4138185
## 957 0.5901989
## 958 0.6141872
## 959 0.4248093
## 960 0.5660774
## 961 0.4308375
## 962 0.4378406
## 963 0.4135766
## 964 0.5390797
## 965 0.5905478
## 966 0.5478158
## 967 0.5680658
## 968 0.5754429
## 969 0.4097403
## 970 0.7499033
## 971 0.5869273
## 972 0.5644726
## 973 0.6005391
## 974 0.5542444
## 975 0.5507464# We can access the estimated deviation between each series average Dim2 and the overall average:
ranef(md_final2)## $Source
## (Intercept) RegisterFiction
## Access -0.876509732933187680 1.397585348817344642
## Achievers 0.216403111752454302 0.715024368667208643
## BBC 0.190537604674936550 -1.012265602908283935
## Book1 -0.001830655015635874 0.018684984308233749
## Book10 0.104402024781476019 -1.065602299793086516
## Book100 -0.034003539723212045 0.347064630269411090
## Book101 0.136663992667235695 -1.394891192866511753
## Book102 0.074226582891395443 -0.757609994637645667
## Book103 -0.103678462819929734 1.058217104995573976
## Book104 0.151590374078454382 -1.547240597896494041
## Book105 0.146261220396889241 -1.492847415092686481
## Book106 0.162207109291765489 -1.655602648185061643
## Book107 -0.057093899508418582 0.582741481764068370
## Book108 -0.155099252893723200 1.583054743676449050
## Book109 -0.047320485048579400 0.482986970804781479
## Book11 0.093886250503439656 -0.958270729565067203
## Book110 0.134636855457011873 -1.374200769690026469
## Book111 0.400510549811615824 -4.087899289922361845
## Book112 -0.073847475885493119 0.753740555340824514
## Book113 -0.038210245359663310 0.390001299458895667
## Book114 -0.075549954401892050 0.771117277930451483
## Book115 0.163670053040159413 -1.670534506317310708
## Book116 0.213928802545201496 -2.183511521556196211
## Book117 -0.155075497379540850 1.582812277779849230
## Book118 0.032882090083265474 -0.335618307097700908
## Book119 -0.008112008890675516 0.082797008467402733
## Book12 -0.085345144749731483 0.871094049294954265
## Book120 -0.034037193368303915 0.347408123622927001
## Book121 -0.072340980361098206 0.738364176398143557
## Book122 0.108990798271025605 -1.112438628819545805
## Book123 -0.061926167018336475 0.632063086230671978
## Book124 -0.026967948272884887 0.275254313893209979
## Book125 0.179126974268116190 -1.828298983038014303
## Book126 -0.056979081189870585 0.581569563263837686
## Book127 -0.034013437854121191 0.347165657726327348
## Book128 -0.091703704045863491 0.935994087618071680
## Book129 -0.115106844767802724 1.174863406751288641
## Book13 0.151764581182458003 -1.549018681138223608
## Book130 -0.000927945476708217 0.009471280237454961
## Book131 -0.077264310675381837 0.788615233468378030
## Book132 -0.164492579126645250 1.678929800290144936
## Book133 -0.066404081441702248 0.677767907739666109
## Book134 -0.047839147108226288 0.488280809547203098
## Book135 -0.036634462918903217 0.373917728317799347
## Book136 -0.082892387910407817 0.846059445471061711
## Book137 -0.073999907101495968 0.755296378177338079
## Book138 0.028645690054086726 -0.292378555537466722
## Book139 -0.105796662834518898 1.079836980775691124
## Book14 0.020871698037925591 -0.213031590875299770
## Book140 -0.034664734867953115 0.353813264391428850
## Book141 -0.076209169920450834 0.777845706561085981
## Book142 -0.004291330359686954 0.043800410097659573
## Book143 -0.028205214636546686 0.287882746007764090
## Book144 -0.113952722703779208 1.163083605274832122
## Book145 -0.223400315420230200 2.280184607382610196
## Book146 -0.028185418374728013 0.287680691093931573
## Book147 -0.049630708802808871 0.506566779249077115
## Book148 0.050374067026173842 -0.514154028960422393
## Book149 0.129759056544910628 -1.324414438921607484
## Book15 -0.093853578079362274 0.957937251260321365
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## Book92 1.4361131025500182279
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## Dogo 0.3443957167058526725
## Ducksters -0.0753534173839083277
## EIM 0.6082591921844688887
## Encyclopedia -1.0821464214575564622
## Factmonster 0.5262268732097243884
## GreenLine -0.0002086143628867998
## History 0.7014833940877389651
## HT -0.8570592554569849142
## JTT -0.3625325679788663491
## NGL 0.0102362637763926956
## POC -0.5429259138278393904
## Quatr -0.0693027321949278019
## Revision -1.2118441325498410244
## Science -0.8620842224289650968
## Science_Tech -2.1345811627242272657
## Solutions 0.9583426523761301441
## Spoken.BNC2014 0.0000000000003884903
## Teen 0.7027989243213272896
## TeenVogue 1.3787839463784812288
## TweenTribute 0.4773825387924300889
## WhyFiles 0.5644858094987162955
## World 0.0793861462983100619
##
## with conditional variances for "Source"## Plot predicted vs. observed values
dimensions_ref[, "predicted"] <- predict(md_final2)
dimensions_ref %>%
ggplot(aes(x = Corpus, y = Dim2)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
geom_point(aes(y = predicted), shape = "circle filled", fill = "red", position = position_jitter(width = 0.2, height = 0)) +
facet_wrap(vars(Register))
## Compare means
comparisons <- emmeans(md_final2, "Corpus", by = "Register")## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.pairs(comparisons)## Register = Conversation:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -1.1520 0.518 Inf -2.223 0.0262
##
## Register = Fiction:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -2.2559 0.562 Inf -4.017 0.0001
##
## Register = Informative:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -0.0027 0.487 Inf -0.006 0.9956
##
## Degrees-of-freedom method: asymptotic# This is a warning that the degrees of freedom have been calculated according to the naive 'asymptotic' method (i.e. are assumed to be infinite), because we have a very large number of observations and so a more complex estimation method like Kenward-Roger might take a lot of computation.
visreg(md_final2, xvar = "Corpus", by="Register", type = "conditional", line=list(col="darkred"), ylab = "Dimension 2 (Biber 1988)")
emmeans(md_final2, ~ Corpus*Register)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Corpus Register emmean SE df asymp.LCL asymp.UCL
## Textbook Conversation -3.10 0.186 Inf -3.47 -2.737
## Reference Conversation -1.95 0.484 Inf -2.90 -1.002
## Textbook Fiction 2.65 0.551 Inf 1.56 3.726
## Reference Fiction 4.90 0.106 Inf 4.69 5.110
## Textbook Informative -1.73 0.388 Inf -2.50 -0.973
## Reference Informative -1.73 0.293 Inf -2.31 -1.157
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95TxBzscores %>% filter(Register=="Fiction") %>%
#filter(Level %in% c("C", "D", "E")) %>%
select(Level, Past_tense, TPP3, Perfect_aspect, Public_verbs, Syn_negation, Present_part_clauses) %>%
group_by(Level) %>%
summarise_if(is.numeric, mean) %>%
mutate_if(is.numeric, round, 2)## # A tibble: 5 × 7
## Level Past_tense TPP3 Perfect_aspect Public_verbs Syn_negation
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 A -0.2 0.99 -1.24 1.14 -0.51
## 2 B 1.86 1.18 -1.24 1.59 -0.34
## 3 C 1.71 1.08 -0.47 0.99 0.17
## 4 D 1.34 1.04 -0.31 0.22 0.08
## 5 E 0.88 1.09 -0.53 0.21 0.12
## # … with 1 more variable: Present_part_clauses <dbl>YFzscores %>%
select(Past_tense, TPP3, Perfect_aspect, Public_verbs, Syn_negation, Present_part_clauses) %>%
summarise_if(is.numeric, mean) %>%
round(2)## Past_tense TPP3 Perfect_aspect Public_verbs Syn_negation Present_part_clauses
## 1 1.49 1.18 -0.26 1.04 0.27 1.19TxBcounts %>% filter(Register=="Fiction") %>%
#filter(Level %in% c("C", "D", "E")) %>%
select(Level, Past_tense, TPP3, Perfect_aspect, Public_verbs, Syn_negation, Present_part_clauses) %>%
group_by(Level) %>%
summarise_if(is.numeric, mean) %>%
mutate_if(is.numeric, round, 2)## # A tibble: 5 × 7
## Level Past_tense TPP3 Perfect_aspect Public_verbs Syn_negation
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 A 3.41 5.21 0.22 1.39 0.09
## 2 B 9.67 5.64 0.21 1.63 0.12
## 3 C 9.22 5.42 0.61 1.3 0.2
## 4 D 8.08 5.33 0.7 0.89 0.18
## 5 E 6.68 5.45 0.58 0.88 0.19
## # … with 1 more variable: Present_part_clauses <dbl>YFcounts %>%
select(Past_tense, TPP3, Perfect_aspect, Public_verbs, Syn_negation, Present_part_clauses) %>%
summarise_if(is.numeric, mean) %>%
round(2)## Past_tense TPP3 Perfect_aspect Public_verbs Syn_negation Present_part_clauses
## 1 8.54 5.64 0.72 1.33 0.21 0.3TxBFictioncounts <- TxBcounts %>% filter(Register=="Fiction")
Fictioncounts <- merge(YFcounts, TxBFictioncounts, all = TRUE)
Fictioncounts$Level <- ifelse(is.na(Fictioncounts$Level), "Youth Fiction", Fictioncounts$Level)
Fictioncounts$Register <- ifelse(is.na(Fictioncounts$Register), "Youth Fiction", Fictioncounts$Register)
Fictioncounts %>%
select(Level, Past_tense, TPP3, Perfect_aspect, Public_verbs, Syn_negation, Present_part_clauses) %>%
group_by(Level) %>%
summarise_if(is.numeric, mean) %>%
mutate_if(is.numeric, round, 2)## # A tibble: 6 × 7
## Level Past_tense TPP3 Perfect_aspect Public_verbs Syn_negation
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 A 3.41 5.21 0.22 1.39 0.09
## 2 B 9.67 5.64 0.21 1.63 0.12
## 3 C 9.22 5.42 0.61 1.3 0.2
## 4 D 8.08 5.33 0.7 0.89 0.18
## 5 E 6.68 5.45 0.58 0.88 0.19
## 6 Youth Fiction 8.54 5.64 0.72 1.33 0.21
## # … with 1 more variable: Present_part_clauses <dbl>Dim2Fiction <- Fictioncounts %>%
select(Level, Past_tense, TPP3, Perfect_aspect, Public_verbs, Syn_negation, Present_part_clauses)
london <- suffrager::suf_palette("london")
hanwell <- suffrager::suf_palette("hanwell")
colours <- c(london, hanwell[3:4])
Dim2FictionFeature <- Dim2Fiction %>% ggplot(aes(x = Level, y = Present_part_clauses, colour = Level, fill = Level)) +
geom_jitter(size=0.7, alpha=.7) +
geom_boxplot(outlier.shape = NA, fatten = 2, fill = "white", alpha = 0.3) +
scale_colour_manual(values = colours) +
theme_minimal() +
theme(legend.position = "none") +
xlab("")
Dim2FictionFeature
Ppc <- Dim2FictionFeature + ylab("Present participial clauses ")
past <- Dim2FictionFeature + aes(y = Past_tense) + ylab("Past tense verbs ")
TPP3 <- Dim2FictionFeature + aes(y = TPP3) + ylab("Third person pronouns ")
SynNeg <- Dim2FictionFeature + aes(y = Syn_negation) + ylim(c(-0.01,0.75)) + ylab("Synthetic negation ") # These y-axis limits remove three outliers that overextend the scale and make the real differences invisible
PerAsp <- Dim2FictionFeature + aes(y = Perfect_aspect) + ylab("Perfect aspect verbs")
public <- Dim2FictionFeature + aes(y = Public_verbs) + ylab("Public verbs")
grid.arrange(past, TPP3, PerAsp, public, SynNeg, Ppc, ncol=2, nrow=3)## Warning: Removed 3 rows containing non-finite values (stat_boxplot).## Warning: Removed 3 rows containing missing values (geom_point).
#ggsave(here("plots", "Dim2FictionFeatures.svg"), dpi = 300)NarrativeTxBdim <- filter(TxBdimensions, Register == "Fiction")
summary(NarrativeTxBdim$Dim1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -20.096 -0.172 5.381 5.027 10.467 27.151summary(NarrativeTxBdim$Dim2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.3971 0.4579 2.5474 2.5253 4.1941 10.6745boxplot(NarrativeTxBdim$Dim2)
boxplot(NarrativeTxBdim$Dim2 ~ NarrativeTxBdim$Level) # We can see that the past tense is probably not taught until level B
boxplot(NarrativeTxBdim$Dim2 ~ NarrativeTxBdim$Series)
boxplot(NarrativeTxBdim$Dim2 ~ NarrativeTxBdim$Country)
options(contrasts=c("contr.treatment", "contr.poly")) # Standard option
options(contrasts=c("contr.sum", "contr.poly")) # Gries 2013: 5.2.1
lm <- lm(Dim2 ~ Series + Level, NarrativeTxBdim)
summary(lm)##
## Call:
## lm(formula = Dim2 ~ Series + Level, data = NarrativeTxBdim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6258 -1.6533 0.0481 1.3697 7.1756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.45785 0.26349 9.328 < 0.0000000000000002 ***
## Series1 0.50173 0.38853 1.291 0.197679
## Series2 1.06356 0.50213 2.118 0.035071 *
## Series3 1.22427 0.81102 1.510 0.132324
## Series4 -0.46067 0.38206 -1.206 0.228958
## Series5 -0.18670 0.60178 -0.310 0.756607
## Series6 0.03592 0.50487 0.071 0.943330
## Series7 -1.06412 0.38634 -2.754 0.006277 **
## Series8 0.45962 1.33908 0.343 0.731684
## Level1 -2.42326 0.37193 -6.515 0.00000000035 ***
## Level2 0.62393 0.31491 1.981 0.048566 *
## Level3 1.07940 0.31871 3.387 0.000812 ***
## Level4 0.49484 0.29039 1.704 0.089512 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.567 on 272 degrees of freedom
## Multiple R-squared: 0.2103, Adjusted R-squared: 0.1754
## F-statistic: 6.035 on 12 and 272 DF, p-value: 0.000000002342TukeyHSD(aov(lm), ordered = TRUE)## Tukey multiple comparisons of means
## 95% family-wise confidence level
## factor levels have been ordered
##
## Fit: aov(formula = lm)
##
## $Series
## diff lwr upr p adj
## NGL-Solutions 0.4848200 -3.2423715 4.212012 0.9999790
## GreenLine-Solutions 1.0628021 -2.6583378 4.783942 0.9931995
## POC-Solutions 1.6051867 -4.2566203 7.466994 0.9948896
## HT-Solutions 1.7460756 -2.3115766 5.803728 0.9164965
## JTT-Solutions 1.8531129 -2.0438396 5.750065 0.8608883
## Access-Solutions 2.1468819 -1.5824525 5.876216 0.6821436
## Achievers-Solutions 2.6250771 -1.2718753 6.522030 0.4711284
## EIM-Solutions 3.0277867 -1.4492424 7.504816 0.4654428
## GreenLine-NGL 0.5779821 -0.8249622 1.980926 0.9341260
## POC-NGL 1.1203667 -3.6211681 5.861901 0.9981756
## HT-NGL 1.2612556 -0.8802186 3.402730 0.6542882
## JTT-NGL 1.3682929 -0.4503904 3.186976 0.3147804
## Access-NGL 1.6620619 0.2375248 3.086599 0.0094684
## Achievers-NGL 2.1402571 0.3215739 3.958940 0.0084802
## EIM-NGL 2.5429667 -0.3145077 5.400441 0.1253146
## POC-GreenLine 0.5423846 -4.1943946 5.279164 0.9999922
## HT-GreenLine 0.6832735 -1.4476505 2.814197 0.9854194
## JTT-GreenLine 0.7903108 -1.0159379 2.596559 0.9087775
## Access-GreenLine 1.0840798 -0.3245477 2.492707 0.2846289
## Achievers-GreenLine 1.5622751 -0.2439736 3.368524 0.1510745
## EIM-GreenLine 1.9649846 -0.8845917 4.814561 0.4374723
## HT-POC 0.1408889 -4.8645818 5.146360 1.0000000
## JTT-POC 0.2479262 -4.6281823 5.124035 1.0000000
## Access-POC 0.5416952 -4.2015242 5.284915 0.9999923
## Achievers-POC 1.0198905 -3.8562180 5.895999 0.9992433
## EIM-POC 1.4226000 -3.9284732 6.773673 0.9958294
## JTT-HT 0.1070373 -2.3178737 2.531948 1.0000000
## Access-HT 0.4008063 -1.7443954 2.546008 0.9996708
## Achievers-HT 0.8790016 -1.5459094 3.303913 0.9686381
## EIM-HT 1.2817111 -1.9951386 4.558561 0.9508669
## Access-JTT 0.2937690 -1.5293018 2.116840 0.9998917
## Achievers-JTT 0.7719643 -1.3732374 2.917166 0.9699916
## EIM-JTT 1.1746738 -1.9009467 4.250294 0.9572116
## Achievers-Access 0.4781952 -1.3448757 2.301266 0.9961996
## EIM-Access 0.8809048 -1.9793642 3.741174 0.9887762
## EIM-Achievers 0.4027095 -2.6729110 3.478330 0.9999779
##
## $Level
## diff lwr upr p adj
## E-A 2.5476502 1.1087512 3.986549 0.0000193
## D-A 2.8549365 1.4056483 4.304225 0.0000014
## B-A 3.0196265 1.5084193 4.530834 0.0000009
## C-A 3.4452044 1.9227590 4.967650 0.0000000
## D-E 0.3072864 -0.8802712 1.494844 0.9539704
## B-E 0.4719763 -0.7904039 1.734357 0.8428572
## C-E 0.8975542 -0.3782579 2.173366 0.3029163
## B-D 0.1646899 -1.1095197 1.438900 0.9965865
## C-D 0.5902679 -0.6972503 1.877786 0.7165284
## C-B 0.4255779 -0.9312617 1.782418 0.9106869par(mfrow = c(1,2))
car::crPlot(lm, var = "Series")
car::crPlot(lm, var = "Level")
par(mfrow = c(1,1))
car::influencePlot(lm, id.method = "identify") # six outliers are identified of which: ## Warning in plot.window(...): "id.method" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "id.method" is not a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is not
## a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is not
## a graphical parameter## Warning in box(...): "id.method" is not a graphical parameter## Warning in title(...): "id.method" is not a graphical parameter## Warning in plot.xy(xy.coords(x, y), type = type, ...): "id.method" is not a
## graphical parameter
## StudRes Hat CookD
## 100 -2.7858779 0.12017185 0.07956506
## 194 2.8953873 0.04254017 0.02789440
## 268 2.8510645 0.02699267 0.01690305
## 279 0.5957952 0.34666809 0.01452318
## 280 -0.9925064 0.34977239 0.04076311
## 284 -1.9685634 0.20967290 0.07825718# 273 is odd because it is a second-person narrative account but it was decided not to remove it because it is still a well-formed narrative text
# The others were fine.
NarrativeTxBdim <- NarrativeTxBdim[-268,] # 268 (POC_4e_PB_cleaned_Narrative_maxed_0002) was identified as to be removed because it is a rhymed version of Cindrella InformativeTxBDim <- filter(TxBdimensions, Register == "Informative")
summary(InformativeTxBDim$Dim1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -23.5521 -10.1986 -5.5381 -5.2606 -0.8507 21.3275summary(InformativeTxBDim$Dim2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -6.9917 -3.5551 -1.6605 -1.5110 0.1062 9.3885boxplot(InformativeTxBDim$Dim2)
boxplot(InformativeTxBDim$Dim2 ~ InformativeTxBDim$Level) # We can see that the past tense is probably not taught until level B
boxplot(InformativeTxBDim$Dim2 ~ InformativeTxBDim$Series)
boxplot(InformativeTxBDim$Dim2 ~ InformativeTxBDim$Country)
options(contrasts=c("contr.treatment", "contr.poly")) # Standard option
options(contrasts=c("contr.sum", "contr.poly")) # Gries 2013: 5.2.1
lm <- lm(Dim1 ~ Series + Level, InformativeTxBDim)
summary(lm)##
## Call:
## lm(formula = Dim1 ~ Series + Level, data = InformativeTxBDim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.7425 -3.9219 -0.4992 3.1995 30.9337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.6086 0.3990 -14.057 < 0.0000000000000002 ***
## Series1 -0.5151 1.0039 -0.513 0.60822
## Series2 3.6624 0.7846 4.668 0.0000043396892 ***
## Series3 5.9228 0.9010 6.573 0.0000000001787 ***
## Series4 3.5027 1.0721 3.267 0.00119 **
## Series5 -7.2635 1.0421 -6.970 0.0000000000159 ***
## Series6 -5.4580 0.9502 -5.744 0.0000000200912 ***
## Series7 2.4136 1.1356 2.125 0.03425 *
## Series8 -3.9570 1.6382 -2.415 0.01623 *
## Level1 1.8786 0.9300 2.020 0.04416 *
## Level2 1.4603 0.7283 2.005 0.04573 *
## Level3 1.0231 0.6333 1.615 0.10713
## Level4 -1.4437 0.6072 -2.378 0.01796 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.207 on 350 degrees of freedom
## Multiple R-squared: 0.3432, Adjusted R-squared: 0.3207
## F-statistic: 15.24 on 12 and 350 DF, p-value: < 0.00000000000000022TukeyHSD(aov(lm), ordered = TRUE)## Tukey multiple comparisons of means
## 95% family-wise confidence level
## factor levels have been ordered
##
## Fit: aov(formula = lm)
##
## $Series
## diff lwr upr p adj
## JTT-HT 2.4126369 -2.1826794 7.007953 0.7827287
## POC-HT 4.5281635 -2.0304927 11.086820 0.4377118
## Access-HT 6.4473497 1.7083953 11.186304 0.0009244
## NGL-HT 8.9804284 3.9172494 14.043607 0.0000022
## Solutions-HT 10.1893773 6.1293673 14.249387 0.0000000
## GreenLine-HT 10.7380069 5.8140485 15.661965 0.0000000
## Achievers-HT 11.0074564 6.8014427 15.213470 0.0000000
## EIM-HT 13.3890380 8.9086228 17.869453 0.0000000
## POC-JTT 2.1155267 -4.2617491 8.492802 0.9821819
## Access-JTT 4.0347129 -0.4498566 8.519282 0.1166258
## NGL-JTT 6.5677915 1.7418765 11.393706 0.0009192
## Solutions-JTT 7.7767404 4.0167730 11.536708 0.0000000
## GreenLine-JTT 8.3253700 3.6457290 13.005011 0.0000020
## Achievers-JTT 8.5948195 4.6776489 12.511990 0.0000000
## EIM-JTT 10.9764011 6.7659624 15.186840 0.0000000
## Access-POC 1.9191862 -4.5623571 8.400729 0.9914950
## NGL-POC 4.4522648 -2.2699741 11.174504 0.4975122
## Solutions-POC 5.6612137 -0.3418184 11.664246 0.0821511
## GreenLine-POC 6.2098433 -0.4081685 12.827855 0.0854559
## Achievers-POC 6.4792929 0.3765668 12.582019 0.0279197
## EIM-POC 8.8608744 2.5658896 15.155859 0.0004986
## NGL-Access 2.5330786 -2.4298049 7.495962 0.8081843
## Solutions-Access 3.7420275 -0.1921957 7.676251 0.0767468
## GreenLine-Access 4.2906571 -0.5301102 9.111424 0.1256594
## Achievers-Access 4.5601067 0.4753811 8.644832 0.0160566
## EIM-Access 6.9416883 2.5749329 11.308444 0.0000382
## Solutions-NGL 1.2089489 -3.1103340 5.528232 0.9941766
## GreenLine-NGL 1.7575785 -3.3822551 6.897412 0.9784273
## Achievers-NGL 2.0270280 -2.4297729 6.483829 0.8897887
## EIM-NGL 4.4086096 -0.3080252 9.125244 0.0880629
## GreenLine-Solutions 0.5486296 -3.6065827 4.703842 0.9999770
## Achievers-Solutions 0.8180791 -2.4546834 4.090842 0.9973363
## EIM-Solutions 3.1996607 -0.4189776 6.818299 0.1313614
## Achievers-GreenLine 0.2694495 -4.0285337 4.567433 0.9999999
## EIM-GreenLine 2.6510311 -1.9158309 7.217893 0.6743085
## EIM-Achievers 2.3815816 -1.4001394 6.163303 0.5684244
##
## $Level
## diff lwr upr p adj
## D-E 1.1097074 -1.3993469 3.618762 0.7439926
## C-E 3.4127499 0.8245059 6.000994 0.0031498
## B-E 3.8653419 0.9879449 6.742739 0.0024593
## A-E 4.2130541 0.6884706 7.737638 0.0100774
## C-D 2.3030425 -0.1982833 4.804368 0.0875163
## B-D 2.7556345 -0.0438364 5.555105 0.0560855
## A-D 3.1033467 -0.3579122 6.564606 0.1026567
## B-C 0.4525920 -2.4180684 3.323252 0.9927083
## A-C 0.8003041 -2.7187819 4.319390 0.9712428
## A-B 0.3477122 -3.3891813 4.084606 0.9990635par(mfrow = c(1,2))
car::crPlot(lm, var = "Series")
car::crPlot(lm, var = "Level") # Higher levels have lower scores on Dim1
par(mfrow = c(1,1))
car::influencePlot(lm, id.method = "identify")## Warning in plot.window(...): "id.method" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "id.method" is not a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is not
## a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is not
## a graphical parameter## Warning in box(...): "id.method" is not a graphical parameter## Warning in title(...): "id.method" is not a graphical parameter## Warning in plot.xy(xy.coords(x, y), type = type, ...): "id.method" is not a
## graphical parameter
## StudRes Hat CookD
## 218 5.2635436 0.03525890 0.0723665396
## 283 -0.3669127 0.10822991 0.0012599455
## 284 0.2753486 0.10822991 0.0007096842
## 286 3.1332706 0.04000724 0.0306985365colours <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
colours2 <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
colours <- c(colours, colours2[c(2:4)]) # Nine colours range
#scales::show_col(colours)
# Plot all registers on one dimension
p2 <- ggplot(TxBdimensions,aes(x=Register,y=Dim1))+
geom_flat_violin(position = position_nudge(x = .2, y = 0),adjust =2)+
geom_point(position = position_jitter(width = .15), size = .25)+
ylab('Dimension 1 (Biber 1988)')+xlab('Textbook Registers')+theme_cowplot()+
ggtitle('Dimension 1')
p2
# Plot with coordinate flip
p3 <- ggplot(TxBdimensions,aes(x=Register,y=Dim1))+
geom_flat_violin(position = position_nudge(x = .2, y = 0),adjust = 2)+
geom_point(position = position_jitter(width = .15), size = .25)+
ylab('Dimension 1 (Biber 1988)')+xlab('Textbook Registers')+coord_flip()+theme_cowplot()+guides(fill = "none")+
ggtitle('Dimension 1')
p3
# Comparing textbook series
london <- suf_palette(name = "london", n = 5, type = "continuous") #
hanwell <- suf_palette(name = "hanwell", n = 5, type = "discrete") #
colours <- c(hanwell, london) # Better for a discrete palette
Fiction <- dimensions_ref %>% filter(Register=="Fiction")
p8 <- ggplot(Fiction,aes(x=Corpus,y=Dim2, fill = Corpus))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .45, aes(x=Corpus,y=Dim2, fill = Corpus, colour = Level))+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Corpus)+0.25, y = Dim2), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Score on Biber\'s (1988) Dimension 2')+ theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours[c(10,2)])+
ggtitle("Dimension 2: Narrative vs. Non-narrative Concerns")
p8
# Comparing textbook series without Level A
Fiction.B.E <- Fiction %>% filter(!Level=="A")
summary(Fiction.B.E$Level)## A B C D E
## 0 55 53 69 72
## Info Teens Spoken BNC2014 Youth Fiction
## 0 0 1191london <- suf_palette(name = "london", n = 5, type = "continuous") #
hanwell <- suf_palette(name = "hanwell", n = 5, type = "discrete") #
colours <- c(hanwell, london) # Better for a discrete palette
p8a <- ggplot(Fiction.B.E,aes(x=Corpus,y=Dim2, fill = Corpus))+
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .45, aes(x=Corpus,y=Dim2, fill = Corpus, colour = Level))+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Corpus)+0.25, y = Dim2), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Score on Biber\'s (1988) Dimension 2')+ theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours[c(10,2)])+
ggtitle("Dimension 2: Narrative vs. Non-narrative Concerns")
p8a
Dimension 3: Explicit vs. Situation-Dependent Reference
# Rainplots for all textbook registers and the three ref. corpora on Dimension 3
Dimensions[Dimensions$Dim3>20,] # Remove four Info Teens outliers with very high Dim 3 scores## Corpus
## 3691 Informative.Teens
## 3960 Informative.Teens
## 4314 Informative.Teens
## 4578 Informative.Teens
## Filename
## 3691 Revision_World_GCSE_10529068_wjec-level-law-past-papers.txt
## 3960 Teen_Kids_News_10405570_how-to-become-a-conservation-scientist.txt
## 4314 Revision_World_GCSE_10526584_unit-10-investigating-ict-business.txt
## 4578 Revision_World_GCSE_10528474_wjec-level-history-past-papers.txt
## Level Dim1 Dim2 Dim3 Dim4 Dim5 Dim6
## 3691 Info Teens -18.9903 -7.3784 32.1621 -9.2659 -3.9201 -3.0246
## 3960 Info Teens -17.9705 -6.1316 24.4966 3.2431 -1.5149 -2.4668
## 4314 Info Teens -16.2157 -0.3315 23.1731 -3.7492 1.6543 -3.1060
## 4578 Info Teens -26.6036 -7.3784 27.1940 -9.2659 -3.9201 -3.0653
## TextType Register Series Country
## 3691 Learned exposition Info Teens Info Teens Info Teens
## 3960 Learned exposition Info Teens Info Teens Info Teens
## 4314 Learned exposition Info Teens Info Teens Info Teens
## 4578 Learned exposition Info Teens Info Teens Info TeensDimensions3 <- Dimensions[Dimensions$Dim3<20,]
colours <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
colours2 <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
colours <- c(colours, colours2[c(2:4)]) # Nine colours range
#scales::show_col(colours)
p3 <- ggplot(Dimensions3,aes(x=Register,y=Dim3, fill = Register, colour = Register))+ #Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
#note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim3), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 3 (Biber 1988)')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
annotate(geom = "text", x = 8.3, y = -15, label = "Reference Corpora", size = 5) +
annotate(geom = "segment", x = 7, xend = 9.5, y = -13, yend = -13) +
annotate(geom = "text", x = 3.8, y = -15, label = "Textbook Corpus", size = 5) +
annotate(geom = "segment", x = 1, xend = 6.5, y = -13, yend = -13) +
ggtitle("Dimension 3: Explicit vs. Situation-Dependent Reference ")
p3 + scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 20, by = 5))
#ggsave(here("plots", "Dim3.svg"), width = 13, height = 8)london <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
#scales::show_col(london)
classic <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
#scales::show_col(classic)
colours <- c(london[c(6,1)], classic[4], london[2], classic[2], london[3])
#scales::show_col(colours)
dimensions_ref[dimensions_ref$Dim3>20,] # Remove four Info Teens outliers with very high Dim 3 scores## Filename
## 2875 Revision_World_GCSE_10526584_unit-10-investigating-ict-business.txt
## 2920 Revision_World_GCSE_10528474_wjec-level-history-past-papers.txt
## 2939 Revision_World_GCSE_10529068_wjec-level-law-past-papers.txt
## 4706 Teen_Kids_News_10405570_how-to-become-a-conservation-scientist.txt
## Corpus.x Level Dim1 Dim2 Dim3 Dim4 Dim5
## 2875 Informative.Teens Info Teens -16.2157 -0.3315 23.1731 -3.7492 1.6543
## 2920 Informative.Teens Info Teens -26.6036 -7.3784 27.1940 -9.2659 -3.9201
## 2939 Informative.Teens Info Teens -18.9903 -7.3784 32.1621 -9.2659 -3.9201
## 4706 Informative.Teens Info Teens -17.9705 -6.1316 24.4966 3.2431 -1.5149
## Dim6 TextType Subcorpus Register Series
## 2875 -3.1060 Learned exposition Reference_Informative Informative Info Teens
## 2920 -3.0653 Learned exposition Reference_Informative Informative Info Teens
## 2939 -3.0246 Learned exposition Reference_Informative Informative Info Teens
## 4706 -2.4668 Learned exposition Reference_Informative Informative Info Teens
## Country Corpus.y Dim1nopunct Corpus Source predicted
## 2875 Info Teens <NA> -16.2157 Reference Revision -3.2931047
## 2920 Info Teens <NA> -26.6036 Reference Revision -3.2931047
## 2939 Info Teens <NA> -18.9903 Reference Revision -3.2931047
## 4706 Info Teens <NA> -17.9705 Reference Teen -0.8260256Dimensions3 <- dimensions_ref[dimensions_ref$Dim3<20,]
ggplot(Dimensions3,aes(x=Subcorpus,y=Dim3, fill = Subcorpus, colour = Subcorpus))+ # Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
# note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Subcorpus)+0.25, y = Dim3), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 3 (Biber 1988)')+
theme_cowplot()+
theme(axis.title.x=element_blank())+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours3Reg)+
scale_fill_manual(values = colours3Reg) +
annotate(geom = "text", x = 1.5, y = -14, label = "Conversation", size = 5) +
annotate(geom = "segment", x = 0.7, xend = 2.5, y = -12, yend = -12) +
annotate(geom = "text", x = 3.5, y = -14, label = "Fiction", size = 5) +
annotate(geom = "segment", x = 2.7, xend = 4.5, y = -12, yend = -12) +
annotate(geom = "text", x = 5.7, y = -14, label = "Informative", size = 5) +
annotate(geom = "segment", x = 4.7, xend = 6.5, y = -12, yend = -12) +
scale_x_discrete(labels=rep(c("Reference", "Textbook"), 3))+
scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 20, by = 2))
#ggsave(here("plots", "Dim3_3RegComparison.svg"), width = 13, height = 8)Singularity
# Mixed effect model
# Check distribution of the outcome variable.
ggplot(dimensions_ref, aes(x = Dim3)) +
geom_histogram(bins = 20) +
facet_grid(rows = vars(Register), cols = vars(Corpus), scales = "free_y")
# Compute model with maximal random effect structure
md_maximal <- lmer(Dim3 ~ 1 + Corpus + Register + Corpus*Register + (Register|Source), dimensions_ref, REML = FALSE)
# Model failed to converge!
# Calculate variance-covariance matrix for the random effect estimates.
VarCorr(md_maximal)
md_max_ranefs <- tidy(md_maximal, effects = "ran_vals")
head(md_max_ranefs)
# To look at the association between intercepts and slopes, we need the values in the `term` column as separate columns.
md_max_ranefs %>%
pivot_wider(
id_cols = c(group, level),
names_from = term,
values_from = estimate
) ->
md_final_ranefs
head(md_final_ranefs)
# Now we can plot a matrix of correlations among the estimated random effects.
md_final_ranefs %>%
select(`(Intercept)`, starts_with("Register")) %>%
ggpairs(
lower = list(continuous = wrap("smooth_lm", shape = "circle filled", fill = "grey", se = FALSE)),
diag = "blank",
upper = list(continuous = wrap("cor", stars = FALSE))
)
fig_ranef_cor <- ggplot(md_final_ranefs,
aes(x = RegisterFiction, y = RegisterInformative, label = level)) +
geom_point(shape = "bullet") +
geom_text(hjust = "inward")
fig_ranef_cor# Mixed effect model with simplified random effect structure, otherwise the model did not converge (see chunk above)
md0 <- lmer(Dim3 ~ 1 + (Register|Source), dimensions_ref, REML = FALSE)
md_corpus <- update(md0, .~. + Corpus)
md_register <- update(md0, . ~ . + Register)
md_both <- update(md_corpus, .~. + Register)## boundary (singular) fit: see ?isSingularmd_interaction <- update(md_both, . ~ . + Corpus:Register) ## boundary (singular) fit: see ?isSingularanova(md0, md_corpus, md_both, md_interaction)## Data: dimensions_ref
## Models:
## md0: Dim3 ~ 1 + (Register | Source)
## md_corpus: Dim3 ~ (Register | Source) + Corpus
## md_both: Dim3 ~ (Register | Source) + Corpus + Register
## md_interaction: Dim3 ~ (Register | Source) + Corpus + Register + Corpus:Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 8 23239 23291 -11612 23223
## md_corpus 9 23241 23300 -11612 23223 0.2221 1 0.6375
## md_both 11 23188 23260 -11583 23166 57.2586 2 0.0000000000003685
## md_interaction 13 23191 23276 -11582 23165 1.0821 2 0.5821
##
## md0
## md_corpus
## md_both ***
## md_interaction
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(md0, md_register)## Data: dimensions_ref
## Models:
## md0: Dim3 ~ 1 + (Register | Source)
## md_register: Dim3 ~ (Register | Source) + Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 8 23239 23291 -11612 23223
## md_register 10 23190 23255 -11585 23170 53.507 2 0.000000000002405 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1md_final3 <- lmer(Dim3 ~ 1 + Corpus + Register + Corpus:Register + (1|Source), dimensions_ref)
tab_model(md_final3)| Dim 3 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.75 | 0.27 – 1.22 | 0.002 |
| Corpus1 | 0.08 | -0.40 – 0.55 | 0.751 |
| Register1 | -1.54 | -2.20 – -0.87 | <0.001 |
| Register2 | -1.47 | -1.83 – -1.11 | <0.001 |
| Corpus1 * Register1 | 0.31 | -0.35 – 0.98 | 0.358 |
| Corpus1 * Register2 | 0.18 | -0.18 – 0.54 | 0.323 |
| Random Effects | |||
| σ2 | 5.65 | ||
| τ00 Source | 0.99 | ||
| ICC | 0.15 | ||
| N Source | 325 | ||
| Observations | 5033 | ||
| Marginal R2 / Conditional R2 | 0.459 / 0.539 | ||
# Diagnostic plots (in new window)
residplot(md_final3) # Oh dear, oh dear...
# We can access the estimated deviation between each series average Dim3 and the overall average:
ranef(md_final3) %>% head()## $Source
## (Intercept)
## Access -0.512929877373465737
## Achievers -0.844371056651336827
## BBC -0.254229409098271852
## Book1 -0.269611763742281130
## Book10 0.544690150441830001
## Book100 0.428512666342395376
## Book101 -0.548380120311670272
## Book102 0.231482072182613796
## Book103 0.041827009818413578
## Book104 -0.348263529584699805
## Book105 -0.390500535934290849
## Book106 1.134568107604746912
## Book107 -0.078033096851199801
## Book108 -0.999223645467507193
## Book109 0.379373314337525525
## Book11 0.146596593274473580
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## Book111 1.357366773100555202
## Book112 0.462530635168040050
## Book113 -0.434599426879418771
## Book114 -0.233083851041989670
## Book115 0.390616628497316765
## Book116 0.322734990674386935
## Book117 1.396908675781467934
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## Book12 0.072429809109702606
## Book120 -0.116762353769419402
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## Book166 0.002789153243475054
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## Book197 0.385535020816679175
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## Book2 0.891288711558029245
## Book20 0.345890251583526986
## Book200 -0.726133522581745683
## Book201 0.872783018810120326
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## Book205 0.187012861649423046
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## Book210 0.762551221430135873
## Book211 0.446833199296272909
## Book212 -0.168895122444462253
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## Book214 -0.475848914327509365
## Book215 -0.090623962845330169
## Book216 -0.765233099088027835
## Book217 0.321850338325126106
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## Book219 -1.049833989169403736
## Book22 -0.705776231893523454
## Book220 -0.390150789656675989
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## Book228 0.005391677015137698
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## Book25 -0.937904893677465878
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## Book256 1.837928445198420579
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## Book262 0.630626934607492529
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## Book266 -0.333605045890553009
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## Book269 -0.630261895894413282
## Book27 1.096137163688022387
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## Book279 0.661968306650228766
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## Book280 -0.652830817455787527
## Book281 0.009897232003233312
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## Book287 0.428204066685676454
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## Book290 0.717032772064101209
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## Book292 -0.229915561233008992
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## Book294 -0.348489835999627129
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## Book296 0.660137282020363370
## Book297 1.227981223693552337
## Book298 -0.420877028810652698
## Book299 0.188926179521080123
## Book3 1.502676064794252087
## Book30 -0.497101144020216212
## Book300 -0.186547022808782270
## Book31 0.331375781062515529
## Book32 -0.563069463971489048
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## Book34 0.105717425414445815
## Book35 -0.181959174578894961
## Book36 -0.358725057947470005
## Book37 -0.374309340611773456
## Book38 -0.064351845403329430
## Book39 1.478605291570179503
## Book4 0.209921242833187999
## Book40 0.744395274959841813
## Book41 0.172138358195572994
## Book42 0.158364526850686943
## Book43 -0.187904861298345383
## Book44 -0.162023636754855177
## Book45 0.182363293488191913
## Book46 0.009989811900249087
## Book47 -0.232703244798702913
## Book48 0.918990674076160863
## Book49 -0.133827914785973445
## Book5 1.221942957077086112
## Book50 0.060919041914088305
## Book51 0.182291286901624189
## Book52 -0.440689126772004780
## Book53 -0.995880482519719612
## Book54 0.119552976690675716
## Book55 -0.130237872112810515
## Book56 -0.135494352932255480
## Book57 -0.133179855506863870
## Book58 0.246140555876760703
## Book59 -0.395294117268657264
## Book6 1.268603225172981075
## Book60 0.030799715418325518
## Book61 0.248033300437969811
## Book62 0.250327224552913452
## Book63 -1.000540337336174534
## Book64 0.016408684760001749
## Book65 0.333391965486412201
## Book66 -0.747797218483410964
## Book67 0.173917949549318551
## Book68 0.299332850039871756
## Book69 -0.432305502764475269
## Book7 1.096075443756678869
## Book70 -0.029696103953799079
## Book71 -0.391148595213400396
## Book72 0.650807285732229235
## Book73 -0.637946027346713573
## Book74 -0.353190837436977934
## Book75 -0.063086586810782011
## Book76 0.867382524817540079
## Book77 0.911450555796996187
## Book78 0.154219004795429965
## Book79 0.679116160908574629
## Book8 0.786858587724359904
## Book80 -0.140339367542741955
## Book81 0.211669974221261631
## Book82 -0.461612183497544848
## Book83 -0.491268610508229397
## Book84 -0.631835754143679429
## Book85 1.090366350107379256
## Book86 -0.041392030943444645
## Book87 -0.014194114531287286
## Book88 0.319268387863911485
## Book89 0.319587274175854363
## Book9 0.549977491226946835
## Book90 -0.006540843044659026
## Book91 -0.524679666675660128
## Book92 1.521315484060072665
## Book93 0.175090628244850305
## Book94 -0.982692990522599397
## Book95 -0.416001154234494297
## Book96 -0.161046404508578822
## Book97 0.786529414757192957
## Book98 -0.255436752843660464
## Book99 -0.184818864731156551
## Dogo -1.916247803659451021
## Ducksters -0.215161608057834186
## EIM -1.765756592381979351
## Encyclopedia 1.707249833406061290
## Factmonster 0.952040853348859906
## GreenLine -0.727711917576328693
## History 0.292077550984963352
## HT 1.739707903731532168
## JTT 1.316860861544931538
## NGL -0.017553948579113889
## POC 1.206735107461293532
## Quatr -3.064597873168775077
## Revision 3.588216470124832469
## Science 0.359863302474250302
## Science_Tech 1.713372533358479055
## Solutions -0.394980480175962823
## Spoken.BNC2014 -0.000000000005700134
## Teen -0.283416517460975848
## TeenVogue -0.659273488848748701
## TweenTribute 0.075701187157177219
## WhyFiles -1.322429145969230113
## World -0.973165884592979258## Plot predicted vs. observed values
dimensions_ref[, "predicted"] <- predict(md_final3)
dimensions_ref %>%
ggplot(aes(x = Corpus, y = Dim3)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
geom_point(aes(y = predicted), shape = "circle filled", fill = "red", position = position_jitter(width = 0.2, height = 0)) +
facet_wrap(vars(Register))
## Compare means
comparisons <- emmeans(md_final3, "Corpus", by = "Register")## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.pairs(comparisons)## Register = Conversation:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference 0.777 1.056 Inf 0.736 0.4618
##
## Register = Fiction:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference 0.517 0.376 Inf 1.377 0.1684
##
## Register = Informative:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -0.832 0.444 Inf -1.876 0.0606
##
## Degrees-of-freedom method: asymptotic# This is a warning that the degrees of freedom have been calculated according to the naive 'asymptotic' method (i.e. are assumed to be infinite), because we have a very large number of observations and so a more complex estimation method like Kenward-Roger might take a lot of computation.
visreg(md_final3, xvar = "Corpus", by="Register", type = "conditional", line=list(col="darkred"), ylab = "Dimension 2 (Biber 1988)")
emmeans(md_final3, ~ Corpus*Register)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Corpus Register emmean SE df asymp.LCL asymp.UCL
## Textbook Conversation -0.403 0.3493 Inf -1.09 0.281
## Reference Conversation -1.180 0.9963 Inf -3.13 0.772
## Textbook Fiction -0.467 0.3647 Inf -1.18 0.248
## Reference Fiction -0.984 0.0898 Inf -1.16 -0.808
## Textbook Informative 3.341 0.3558 Inf 2.64 4.038
## Reference Informative 4.173 0.2650 Inf 3.65 4.692
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95Dimension 4 (not discussed in PhD thesis)
# Means and SD
Dimensions %>% select(Register, Dim3) %>% group_by(Register) %>% summarise_if(is.numeric, c(mean = mean, sd = sd))## # A tibble: 9 × 3
## Register mean sd
## <fct> <dbl> <dbl>
## 1 Conversation -0.445 2.37
## 2 Fiction -0.649 2.74
## 3 Informative 3.12 3.07
## 4 Instructional 3.52 2.82
## 5 Personal -0.829 2.96
## 6 Poetry 0.919 3.77
## 7 Info Teens 4.18 3.82
## 8 Spoken BNC2014 -1.18 1.29
## 9 Youth Fiction -0.985 1.62# Rainplots for all textbook registers and the three ref. corpora on Dimension 3
colours <- suf_palette(name = "london", n = 6, type = "continuous") # Very nice, similar to OrRd palette
colours2 <- suf_palette(name = "classic", n = 5, type = "continuous") # Just green and purple
colours <- c(colours, colours2[c(2:4)]) # Nine colours range
#scales::show_col(colours)
p4 <- ggplot(Dimensions,aes(x=Register,y=Dim4, fill = Register, colour = Register))+ #Or leave out "colour = Register" to keep the dots in black
geom_flat_violin(position = position_nudge(x = .25, y = 0),adjust = 2, trim = FALSE)+
geom_point(position = position_jitter(width = .15), size = .25)+
#note that here we need to set the x-variable to a numeric variable and bump it to get the boxplots to line up with the rainclouds.
geom_boxplot(aes(x = as.numeric(Register)+0.25, y = Dim4), outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
ylab('Dimension 4 (Biber 1988)')+
theme_cowplot()+
guides(fill = "none", colour = "none") +
scale_colour_manual(values = colours)+
scale_fill_manual(values = colours)+
annotate(geom = "text", x = 8.3, y = -15, label = "Reference Corpora", size = 5) +
annotate(geom = "segment", x = 7, xend = 9.5, y = -13, yend = -13) +
annotate(geom = "text", x = 3.8, y = -15, label = "Textbook Corpus", size = 5) +
annotate(geom = "segment", x = 1, xend = 6.5, y = -13, yend = -13)
p4 + scale_y_continuous(sec.axis = dup_axis(name=NULL), breaks = seq(from = -10, to = 25, by = 5))
#ggsave(here("plots", "Dim4.svg"), width = 13, height = 8)# Check distribution of the outcome variable.
ggplot(dimensions_ref, aes(x = Dim4)) +
geom_histogram(bins = 20) +
facet_grid(rows = vars(Register), cols = vars(Corpus), scales = "free_y")
md0 <- lmer(Dim4 ~ 1 + (1|Source), dimensions_ref, REML = FALSE) # Random effect had to be simplified otherwise the model did not converge
md_corpus <- update(md0, .~. + Corpus)
md_register <- update(md0, . ~ . + Register)
md_both <- update(md_corpus, .~. + Register)
md_interaction <- update(md_both, . ~ . + Corpus:Register)
anova(md0, md_corpus, md_both, md_interaction)## Data: dimensions_ref
## Models:
## md0: Dim4 ~ 1 + (1 | Source)
## md_corpus: Dim4 ~ (1 | Source) + Corpus
## md_both: Dim4 ~ (1 | Source) + Corpus + Register
## md_interaction: Dim4 ~ (1 | Source) + Corpus + Register + Corpus:Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 3 24864 24884 -12429 24858
## md_corpus 4 24854 24880 -12423 24846 11.825 1 0.0005843 ***
## md_both 6 24826 24865 -12407 24814 32.533 2 0.00000008621 ***
## md_interaction 8 24806 24858 -12395 24790 24.134 2 0.00000574729 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(md0, md_register)## Data: dimensions_ref
## Models:
## md0: Dim4 ~ 1 + (1 | Source)
## md_register: Dim4 ~ (1 | Source) + Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 3 24864 24884 -12429 24858
## md_register 5 24832 24865 -12411 24822 35.998 2 0.00000001525 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1md_final4 <- lmer(Dim4 ~ 1 + Corpus + Register + Corpus:Register + (1|Source), dimensions_ref)
tab_model(md_final4)| Dim 4 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.99 | -1.44 – -0.55 | <0.001 |
| Corpus1 | -0.54 | -0.98 – -0.09 | 0.019 |
| Register1 | 0.56 | -0.07 – 1.18 | 0.080 |
| Register2 | 0.34 | -0.01 – 0.69 | 0.055 |
| Corpus1 * Register1 | -0.35 | -0.97 – 0.28 | 0.275 |
| Corpus1 * Register2 | -0.27 | -0.61 – 0.08 | 0.133 |
| Random Effects | |||
| σ2 | 7.80 | ||
| τ00 Source | 0.86 | ||
| ICC | 0.10 | ||
| N Source | 325 | ||
| Observations | 5033 | ||
| Marginal R2 / Conditional R2 | 0.113 / 0.201 | ||
# Diagnostic plots (in new window)
residplot(md_final4) # Oh dear, oh dear...
## Plot predicted vs. observed values
dimensions_ref[, "predicted"] <- predict(md_final4)
dimensions_ref %>%
ggplot(aes(x = Corpus, y = Dim4)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
geom_point(aes(y = predicted), shape = "circle filled", fill = "red", position = position_jitter(width = 0.2, height = 0)) +
facet_wrap(vars(Register))
## Compare means
comparisons <- emmeans(md_final4, "Corpus", by = "Register")## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.pairs(comparisons)## Register = Conversation:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -1.765 0.987 Inf -1.787 0.0739
##
## Register = Fiction:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -1.602 0.369 Inf -4.337 <.0001
##
## Register = Informative:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference 0.156 0.426 Inf 0.365 0.7149
##
## Degrees-of-freedom method: asymptotic# This is a warning that the degrees of freedom have been calculated according to the naive 'asymptotic' method (i.e. are assumed to be infinite), because we have a very large number of observations and so a more complex estimation method like Kenward-Roger might take a lot of computation.
visreg(md_final4, xvar = "Corpus", by="Register", type = "conditional", line=list(col="darkred"), ylab = "Dimension 4 (Biber 1988)")
emmeans(md_final4, ~ Corpus*Register)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' or larger];
## but be warned that this may result in large computation time and memory use.## Corpus Register emmean SE df asymp.LCL asymp.UCL
## Textbook Conversation -1.317 0.3347 Inf -1.9730 -0.661
## Reference Conversation 0.448 0.9288 Inf -1.3727 2.268
## Textbook Fiction -1.453 0.3564 Inf -2.1519 -0.755
## Reference Fiction 0.149 0.0971 Inf -0.0416 0.339
## Textbook Informative -1.811 0.3440 Inf -2.4848 -1.137
## Reference Informative -1.966 0.2511 Inf -2.4585 -1.474
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95dimensions_refUpper <- dimensions_ref %>% filter(!(Level %in% c("A", "B"))) %>% droplevels()
summary(dimensions_refUpper$Level) # Check that the subsetting has worked## C D E Info Teens Spoken BNC2014
## 262 285 233 1414 1251
## Youth Fiction
## 1191md_final4Upper <- lmer(Dim4 ~ 1 + Corpus + Register + Corpus:Register + (1|Source), dimensions_refUpper)
tab_model(md_final4Upper, wrap.labels = 100)| Dim 4 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.49 | -0.97 – -0.01 | 0.043 |
| Corpus1 | -0.04 | -0.52 – 0.44 | 0.873 |
| Register1 | 0.85 | 0.18 – 1.52 | 0.013 |
| Register2 | 0.28 | -0.09 – 0.65 | 0.142 |
| Corpus1 * Register1 | -0.05 | -0.72 – 0.62 | 0.879 |
| Corpus1 * Register2 | -0.32 | -0.70 – 0.05 | 0.088 |
| Random Effects | |||
| σ2 | 6.59 | ||
| τ00 Source | 0.99 | ||
| ICC | 0.13 | ||
| N Source | 325 | ||
| Observations | 4636 | ||
| Marginal R2 / Conditional R2 | 0.125 / 0.239 | ||
residplot(md_final4Upper) # Oh dear, oh dear...
comparisons <- emmeans(md_final4Upper, "Corpus", by = "Register")## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.pairs(comparisons)## Register = Conversation:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -0.183 1.063 Inf -0.172 0.8637
##
## Register = Fiction:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -0.728 0.397 Inf -1.835 0.0665
##
## Register = Informative:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference 0.675 0.456 Inf 1.482 0.1385
##
## Degrees-of-freedom method: asymptoticvisreg(md_final4Upper, xvar = "Corpus", by="Register", type = "conditional", line=list(col="darkred"), ylab = "Dimension 4 (Biber 1988)")
emmeans(md_final4Upper, ~ Corpus*Register)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.## Corpus Register emmean SE df asymp.LCL asymp.UCL
## Textbook Conversation 0.265 0.3657 Inf -0.4516 0.982
## Reference Conversation 0.448 0.9980 Inf -1.5084 2.404
## Textbook Fiction -0.579 0.3854 Inf -1.3345 0.176
## Reference Fiction 0.149 0.0941 Inf -0.0358 0.333
## Textbook Informative -1.288 0.3693 Inf -2.0117 -0.564
## Reference Informative -1.963 0.2667 Inf -2.4855 -1.440
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95Dimension 5 (not discussed in PhD thesis)
# Mixed effect model
dimensions_refUpper <- dimensions_ref %>% filter(!(Level %in% c("A", "B"))) %>% droplevels()
# Check distribution of the outcome variable.
ggplot(dimensions_refUpper, aes(x = Dim5)) +
geom_histogram(bins = 20) +
facet_grid(rows = vars(Register), cols = vars(Corpus), scales = "free_y")
md0 <- lmer(Dim5 ~ 1 + (1|Source), dimensions_refUpper, REML = FALSE) # Random effect had to be simplified otherwise the model did not converge
md_corpus <- update(md0, .~. + Corpus)
md_register <- update(md0, . ~ . + Register)
md_both <- update(md_corpus, .~. + Register)
md_interaction <- update(md_both, . ~ . + Corpus:Register)
anova(md0, md_corpus, md_both, md_interaction)## Data: dimensions_refUpper
## Models:
## md0: Dim5 ~ 1 + (1 | Source)
## md_corpus: Dim5 ~ (1 | Source) + Corpus
## md_both: Dim5 ~ (1 | Source) + Corpus + Register
## md_interaction: Dim5 ~ (1 | Source) + Corpus + Register + Corpus:Register
## npar AIC BIC logLik deviance Chisq Df
## md0 3 18469 18489 -9231.7 18463
## md_corpus 4 18471 18497 -9231.7 18463 0.0564 1
## md_both 6 18160 18199 -9074.2 18148 315.0133 2
## md_interaction 8 18149 18201 -9066.7 18133 15.0138 2
## Pr(>Chisq)
## md0
## md_corpus 0.8122418
## md_both < 0.00000000000000022 ***
## md_interaction 0.0005493 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(md0, md_register, md_both)## Data: dimensions_refUpper
## Models:
## md0: Dim5 ~ 1 + (1 | Source)
## md_register: Dim5 ~ (1 | Source) + Register
## md_both: Dim5 ~ (1 | Source) + Corpus + Register
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## md0 3 18469 18489 -9231.7 18463
## md_register 5 18160 18192 -9075.1 18150 313.2896 2 <0.0000000000000002
## md_both 6 18160 18199 -9074.2 18148 1.7802 1 0.1821
##
## md0
## md_register ***
## md_both
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1md_final5 <- lmer(Dim5 ~ 1 + Corpus + Register + Corpus:Register + (1|Source), dimensions_refUpper, REML = FALSE)
tab_model(md_final5)| Dim 5 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.74 | -1.06 – -0.42 | <0.001 |
| Corpus1 | -0.16 | -0.49 – 0.16 | 0.327 |
| Register1 | -1.08 | -1.53 – -0.63 | <0.001 |
| Register2 | -0.47 | -0.72 – -0.22 | <0.001 |
| Corpus1 * Register1 | 0.22 | -0.23 – 0.68 | 0.331 |
| Corpus1 * Register2 | 0.13 | -0.12 – 0.38 | 0.313 |
| Random Effects | |||
| σ2 | 2.79 | ||
| τ00 Source | 0.45 | ||
| ICC | 0.14 | ||
| N Source | 325 | ||
| Observations | 4636 | ||
| Marginal R2 / Conditional R2 | 0.359 / 0.448 | ||
# Diagnostic plots (in new window)
residplot(md_final5) # ??
## Plot predicted vs. observed values
dimensions_refUpper[, "predicted"] <- predict(md_final5)
dimensions_refUpper %>%
ggplot(aes(x = Corpus, y = Dim5)) +
geom_point(shape = "circle filled", fill = "grey", position = position_jitter(width = 0.2, height = 0)) +
geom_point(aes(y = predicted), shape = "circle filled", fill = "red", position = position_jitter(width = 0.2, height = 0)) +
facet_wrap(vars(Register))
## Compare means
comparisons <- emmeans(md_final5, "Corpus", by = "Register")## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.pairs(comparisons)## Register = Conversation:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference 0.1252 0.718 Inf 0.174 0.8616
##
## Register = Fiction:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -0.0665 0.265 Inf -0.251 0.8022
##
## Register = Informative:
## contrast estimate SE df z.ratio p.value
## Textbook - Reference -1.0312 0.306 Inf -3.366 0.0008
##
## Degrees-of-freedom method: asymptoticemmeans(md_final5, ~ Corpus*Register)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 4636' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4636)' or larger];
## but be warned that this may result in large computation time and memory use.## Corpus Register emmean SE df asymp.LCL asymp.UCL
## Textbook Conversation -1.760 0.2457 Inf -2.241 -1.278
## Reference Conversation -1.885 0.6747 Inf -3.207 -0.562
## Textbook Fiction -1.243 0.2581 Inf -1.748 -0.737
## Reference Fiction -1.176 0.0621 Inf -1.298 -1.054
## Textbook Informative 0.294 0.2479 Inf -0.192 0.780
## Reference Informative 1.325 0.1799 Inf 0.972 1.677
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95visreg(md_final5, xvar = "Corpus", by="Register", type = "conditional", line=list(col="darkred"), ylab = "Dimension 5 (Biber 1988)")
v <- visreg::visreg(md_final5, xvar = "Corpus", by="Source", type = "conditional", line=list(col="darkred"), ylab = "Dimension 5 (Biber 1988)")
v1 <- subset(v, Source %in% c("HT", "JTT", "NGL", "Access", "POC", "GreenLine", "Solutions", "EiM"))
plot(v1)Package used in this script
#packages.bib <- sapply(1:length(loadedNamespaces()), function(i) toBibtex(citation(loadedNamespaces()[i])))
knitr::write_bib(c(.packages(), "knitr"), "packages.bib")## Warning in utils::citation(..., lib.loc = lib.loc): no date field in DESCRIPTION
## file of package 'suffrager'sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] merTools_0.5.2 arm_1.11-2 MASS_7.3-53.1 predictmeans_1.0.4
## [5] nlme_3.1-152 visreg_2.7.0 forcats_0.5.1 stringr_1.4.0
## [9] dplyr_1.0.7 purrr_0.3.4 readr_2.0.2 tidyr_1.1.4
## [13] tibble_3.1.6 tidyverse_1.3.0 suffrager_0.1.0 sjPlot_2.8.9
## [17] scales_1.1.1 lme4_1.1-27.1 Matrix_1.3-2 here_1.0.1
## [21] gridExtra_2.3 GGally_2.1.1 ggplot2_3.3.5 emmeans_1.5.4
## [25] cowplot_1.1.1 car_3.0-10 carData_3.0-4 broom.mixed_0.2.6
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.4.1 Hmisc_4.5-0
## [4] blme_1.0-5 plyr_1.8.6 TMB_1.7.19
## [7] splines_4.0.3 TH.data_1.0-10 digest_0.6.29
## [10] foreach_1.5.1 htmltools_0.5.2 fansi_0.5.0
## [13] magrittr_2.0.1 checkmate_2.0.0 cluster_2.1.1
## [16] tzdb_0.1.2 openxlsx_4.2.3 modelr_0.1.8
## [19] sandwich_3.0-0 jpeg_0.1-8.1 colorspace_2.0-2
## [22] rvest_1.0.0 haven_2.3.1 xfun_0.29
## [25] crayon_1.4.2 jsonlite_1.7.2 survival_3.2-7
## [28] zoo_1.8-9 iterators_1.0.13 glue_1.6.0
## [31] gtable_0.3.0 sjstats_0.18.1 sjmisc_2.8.6
## [34] abind_1.4-5 mvtnorm_1.1-1 DBI_1.1.1
## [37] ggeffects_1.0.1 Rcpp_1.0.7 xtable_1.8-4
## [40] performance_0.7.2 htmlTable_2.1.0 foreign_0.8-81
## [43] Formula_1.2-4 datawizard_0.2.1 htmlwidgets_1.5.4
## [46] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
## [49] pkgconfig_2.0.3 reshape_0.8.8 farver_2.1.0
## [52] nnet_7.3-15 sass_0.4.0 dbplyr_2.1.0
## [55] utf8_1.2.2 later_1.3.0 tidyselect_1.1.1
## [58] labeling_0.4.2 rlang_0.4.12 reshape2_1.4.4
## [61] effectsize_0.4.4-2 munsell_0.5.0 cellranger_1.1.0
## [64] tools_4.0.3 cli_3.1.0 generics_0.1.1
## [67] sjlabelled_1.1.7 broom_0.7.9 evaluate_0.14
## [70] fastmap_1.1.0 yaml_2.2.1 knitr_1.37
## [73] fs_1.5.2 zip_2.1.1 mime_0.12
## [76] xml2_1.3.3 compiler_4.0.3 pbkrtest_0.5.1
## [79] rstudioapi_0.13 curl_4.3.2 png_0.1-7
## [82] reprex_1.0.0 bslib_0.3.1 stringi_1.7.6
## [85] highr_0.9 parameters_0.13.0.1 lattice_0.20-41
## [88] nloptr_1.2.2.3 vctrs_0.3.8 pillar_1.6.4
## [91] lifecycle_1.0.1 jquerylib_0.1.4 estimability_1.3
## [94] data.table_1.14.2 insight_0.14.5 httpuv_1.6.4
## [97] R6_2.5.1 latticeExtra_0.6-29 promises_1.2.0.1
## [100] rio_0.5.26 codetools_0.2-18 boot_1.3-27
## [103] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.3
## [106] multcomp_1.4-16 mgcv_1.8-34 bayestestR_0.9.0
## [109] hms_1.0.0 grid_4.0.3 rpart_4.1-15
## [112] coda_0.19-4 minqa_1.2.4 rmarkdown_2.11
## [115] snakecase_0.11.0 numDeriv_2016.8-1.1 shiny_1.7.1
## [118] lubridate_1.7.10 base64enc_0.1-3
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