Interaction between Phrasal Structure and Vowel Tenseness in German: An Acoustic and Articulatory Study
Malte Belz, Oksana Rasskazova, Jelena Krivokapic, Christine Mooshammer
2021
options(OutDec= ".")
set.seed(1)Packages needed:
library(ggplot2)
library(lme4)## Loading required package: Matrixlibrary(lmerTest)##
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## See ?effectsTheme for details.library(car)## Registered S3 methods overwritten by 'car':
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## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4library(xtable)
library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.3##
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## intersect, setdiff, setequal, unionlibrary(tidyr)## Warning: package 'tidyr' was built under R version 3.6.2##
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## expand, pack, unpacklibrary(texreg)## Version: 1.36.23
## Date: 2017-03-03
## Author: Philip Leifeld (University of Glasgow)
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## Please cite the JSS article in your publications -- see citation("texreg").##
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## extractlibrary(MuMIn)## Registered S3 method overwritten by 'MuMIn':
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## extractlibrary(knitr)## Warning: package 'knitr' was built under R version 3.6.2library(emmeans)## Warning: package 'emmeans' was built under R version 3.6.11 Acoustic analysis
#### Acoustic analysis ####
# read monosyllabic data
mono <- read.table("mono.txt",sep="\t")
mono$tenseness<-factor(mono$tenseness, levels = c("tense","lax"))
mono$position<-factor(mono$position, levels = c("phrase-medial","phrase-final"))
levels(mono$iteration)<-c("1","1","2","2","3","3","4","4","5","5")
# read disyllabic data
di <- read.table("di.txt",sep="\t")
di$tenseness<-factor(di$tenseness, levels = c("tense","lax"))
di$position<-factor(di$position, levels=c("phrase-medial","phrase-final"))
levels(di$iteration)<-c("1","1","2","2","3","3","4","4","5")
# mono and di contain
# columns 1:16 from emuR
# column 17: subject id
# column 18: phrasal position
# column 19: target word
# column 20: iteration: number of repetition
# column 21: pausedur: duration of the following pause
# column 22: labelsdur: duration of the phonetic segment (not to use: to gather one data point per row, the actual duration of
# e.g., aspiration of /b/, is not included in this column. It can be deducted implicitly by subtracting sylposdur with sylseglab == "bh"
# from labelsdur with labelsfac == "b")
# column 23: labelsfac: phonological phonetic segment (factor coded), comprising segments with and without aspiration
# column 24: sylpos: syllable position (0: onset, N: nucleus, C: coda, O1: first syllable, O2: second syl., N1/N2: first/second nucleus)
# column 25: tenseness: tense or lax
# column 26: sylposdur: duration of syllable constituent (sylpos). It is important to note that this is the sum of both
# the stop closure and stop aspiration (corr. to labelsfac)
# column 27: sylseglabel: actual phonetic label of the syllable position (combinations): [Sth, bh, th]1.1 Data used
Unequal data points for respective target words are due to annotation decisions
Monosyllabic
table(mono[mono$position=="phrase-medial",]$word,mono[mono$position=="phrase-medial",]$sylpos)##
## C N O
## Bahn 47 47 47
## Bann 46 46 46
## Beet 47 47 47
## Bett 49 49 49
## Ruhm 50 50 50
## Rum 50 50 50
## Stiel 48 48 48
## still 50 50 50table(mono[mono$position=="phrase-final",]$word,mono[mono$position=="phrase-final",]$sylpos)##
## C N O
## Bahn 50 50 50
## Bann 52 52 52
## Beet 51 51 51
## Bett 50 50 50
## Ruhm 48 48 48
## Rum 49 49 49
## Stiel 49 49 49
## still 48 48 48addmargins(table(mono$position,mono$sylpos))##
## C N O Sum
## phrase-medial 387 387 387 1161
## phrase-final 397 397 397 1191
## Sum 784 784 784 2352addmargins(table(mono$word,mono$sylpos))##
## C N O Sum
## Bahn 97 97 97 291
## Bann 98 98 98 294
## Beet 98 98 98 294
## Bett 99 99 99 297
## Ruhm 98 98 98 294
## Rum 99 99 99 297
## Stiel 97 97 97 291
## still 98 98 98 294
## Sum 784 784 784 2352m<-addmargins(table(mono$word,mono$sylpos))784 target words were analyzed in total
Disyllabic
table(di[di$position=="phrase-medial",]$word,di[di$position=="phrase-medial",]$sylpos)##
## N1 N2 O1 O2
## Huete 48 48 48 48
## Huette 50 50 50 50
## Miete 49 48 49 49
## Mitte 48 48 48 48table(di[di$position=="phrase-final",]$word,di[di$position=="phrase-final",]$sylpos)##
## N1 N2 O1 O2
## Huete 50 50 50 50
## Huette 48 48 48 48
## Miete 49 49 49 49
## Mitte 50 50 50 50197 target words were analyzed
addmargins(table(di$position,di$sylpos))##
## N1 N2 O1 O2 Sum
## phrase-medial 195 194 195 195 779
## phrase-final 197 197 197 197 788
## Sum 392 391 392 392 1567addmargins(table(di$word,di$sylpos))##
## N1 N2 O1 O2 Sum
## Huete 98 98 98 98 392
## Huette 98 98 98 98 392
## Miete 98 97 98 98 391
## Mitte 98 98 98 98 392
## Sum 392 391 392 392 1567addmargins(table(di$word,di$position))##
## phrase-medial phrase-final Sum
## Huete 192 200 392
## Huette 200 192 392
## Miete 195 196 391
## Mitte 192 200 392
## Sum 779 788 1567d<-addmargins(table(di$word,di$sylpos))Total data excluded
# Monosyllabic: 2 position X 8 targets X 5 iterations X 10 subjects X 3 syllable positions
sprintf("%1.2f%%",round((1 - m[9,4]/(2 * 8 * 5 * 10 * 3))*100,3))## [1] "2.00%"# Disyllabic: 2 position X 4 targets X 5 iterations X 10 subjects X 4 syllable positions
sprintf("%1.2f%%",round((1 - d[5,5]/(2 * 4 * 5 * 10 * 4))*100,3))## [1] "2.06%"# Total total:
sprintf("%1.2f%%",round((1 - (m[9,4]+d[5,5])/((2 * 8 * 5 * 10 * 3) + (2 * 4 * 5 * 10 * 4)))*100,3))## [1] "2.02%"1.2 Analysis of monosyllabic target words
### Analysis ####Normalisation to speech rate: sylposdur (ms) divided by 1000 (sec)., multiplied with (sil/s), results in syllables
meta<-read.table("meta.speech.dur.txt",sep="\t", fileEncoding = "UTF-8")
meta$subject## [1] f1 f2 f3 m1 f4 m2 m3 f5 m4 m5
## Levels: f1 f2 f3 f4 f5 m1 m2 m3 m4 m5# merge data with speech rate
mono<-merge(mono, meta[,c("subject","sprechgeschw")], by = "subject")
di<-merge(di, meta[,c("subject","sprechgeschw")], by = "subject")
# normalisation
mono<-mono %>% group_by(subject) %>%
mutate(sylposdurnorm = sprechgeschw*(sylposdur/1000))
di<-di %>% group_by(subject) %>%
mutate(sylposdurnorm = sprechgeschw*(sylposdur/1000))
head(as.data.frame(mono))## subject labels start end utts
## 1 f1 b 2210.802 2238.177 D_vp01_w1_wav_tg_output_bdl:DP1_Bahn_1_0005
## 2 f1 a: 2243.177 2376.802 D_vp01_w1_wav_tg_output_bdl:DP1_Bahn_1_0005
## 3 f1 n 2376.802 2433.385 D_vp01_w1_wav_tg_output_bdl:DP1_Bahn_1_0005
## 4 f1 b 1796.260 1832.594 D_vp01_w1_wav_tg_output_bdl:DP1_Bahn_2_0074
## 5 f1 a: 1839.573 1967.844 D_vp01_w1_wav_tg_output_bdl:DP1_Bahn_2_0074
## 6 f1 n 1967.844 2010.781 D_vp01_w1_wav_tg_output_bdl:DP1_Bahn_2_0074
## db_uuid session
## 1 1dbcb4ee-dcc2-11e6-9ce0-e15c0fc30b38 D_vp01_w1_wav_tg_output_bdl
## 2 1dbcb4ee-dcc2-11e6-9ce0-e15c0fc30b38 D_vp01_w1_wav_tg_output_bdl
## 3 1dbcb4ee-dcc2-11e6-9ce0-e15c0fc30b38 D_vp01_w1_wav_tg_output_bdl
## 4 1dbcb4ee-dcc2-11e6-9ce0-e15c0fc30b38 D_vp01_w1_wav_tg_output_bdl
## 5 1dbcb4ee-dcc2-11e6-9ce0-e15c0fc30b38 D_vp01_w1_wav_tg_output_bdl
## 6 1dbcb4ee-dcc2-11e6-9ce0-e15c0fc30b38 D_vp01_w1_wav_tg_output_bdl
## bundle start_item_id end_item_id level start_item_seq_idx
## 1 DP1_Bahn_1_0005 43 43 MAU 13
## 2 DP1_Bahn_1_0005 45 45 MAU 15
## 3 DP1_Bahn_1_0005 46 46 MAU 16
## 4 DP1_Bahn_2_0074 43 43 MAU 13
## 5 DP1_Bahn_2_0074 45 45 MAU 15
## 6 DP1_Bahn_2_0074 46 46 MAU 16
## end_item_seq_idx type sample_start sample_end sample_rate position
## 1 13 SEGMENT 106119 107432 48000 phrase-medial
## 2 15 SEGMENT 107673 114086 48000 phrase-medial
## 3 16 SEGMENT 114087 116802 48000 phrase-medial
## 4 13 SEGMENT 86221 87964 48000 phrase-medial
## 5 15 SEGMENT 88300 94456 48000 phrase-medial
## 6 16 SEGMENT 94457 96517 48000 phrase-medial
## word iteration pausedur labelsdur labelsfac sylpos tenseness sylposdur
## 1 Bahn 1 0 27.37500 b O tense 32.37500
## 2 Bahn 1 0 133.62500 a: N tense 133.62500
## 3 Bahn 1 0 56.58333 n C tense 56.58333
## 4 Bahn 2 0 36.33333 b O tense 43.31250
## 5 Bahn 2 0 128.27083 a: N tense 128.27083
## 6 Bahn 2 0 42.93750 n C tense 42.93750
## sylseglabel sprechgeschw sylposdurnorm
## 1 bh 5.53 0.1790338
## 2 a: 5.53 0.7389463
## 3 n 5.53 0.3129058
## 4 bh 5.53 0.2395181
## 5 a: 5.53 0.7093377
## 6 n 5.53 0.2374444mono$sylpos<-factor(mono$sylpos, levels=c("O","N","C"))
# Monosyllabic data without Stiel/still
nostiel<-mono %>% filter(word %in% c("Bahn","Bann","Beet","Bett","Ruhm","Rum")) %>% droplevels()
# Overall model monosyllabic acoustic ####
m1<-lmer(sylposdur ~ position + sylpos + tenseness + (1|subject) + (1|word), data = mono)
plot(allEffects(m1))
qqPlot(resid(m1))
## [1] 1890 1891m2<-lmer(sylposdur ~ position * sylpos + tenseness + (1|subject) + (1|word), data = mono)
anova(m1,m2)## refitting model(s) with ML (instead of REML)## Data: mono
## Models:
## m1: sylposdur ~ position + sylpos + tenseness + (1 | subject) + (1 |
## m1: word)
## m2: sylposdur ~ position * sylpos + tenseness + (1 | subject) + (1 |
## m2: word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m1 8 23784 23830 -11884 23768
## m2 10 23529 23587 -11755 23509 258.82 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m3<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word), data = mono)
anova(m2,m3)## refitting model(s) with ML (instead of REML)## Data: mono
## Models:
## m2: sylposdur ~ position * sylpos + tenseness + (1 | subject) + (1 |
## m2: word)
## m3: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## m3: word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2 10 23529 23587 -11755 23509
## m3 15 23222 23308 -11596 23192 317.27 5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m4<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+position|subject) + (0+position|word), data = mono)## boundary (singular) fit: see ?isSingularanova(m3,m4,refit=F)## Data: mono
## Models:
## m3: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## m3: word)
## m4: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## m4: word) + (0 + position | subject) + (0 + position | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m3 15 23172 23258 -11571 23142
## m4 21 23152 23273 -11555 23110 31.364 6 2.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(allEffects(m4))
summary(m4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## word) + (0 + position | subject) + (0 + position | word)
## Data: mono
##
## REML criterion at convergence: 23110.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7961 -0.6901 -0.0360 0.5685 4.3391
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject positionphrase-medial 5.288e+01 7.272055
## positionphrase-final 2.326e+01 4.823273 -0.13
## subject.1 (Intercept) 5.164e+01 7.186317
## word positionphrase-medial 1.305e+02 11.424254
## positionphrase-final 2.306e+02 15.185981 1.00
## word.1 (Intercept) 3.890e-06 0.001972
## Residual 1.079e+03 32.850649
## Number of obs: 2352, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 96.426 6.979 11.205 13.817
## positionphrase-final 9.897 4.808 29.080 2.059
## sylposN 3.725 3.353 2315.958 1.111
## sylposC -31.967 3.353 2315.958 -9.534
## tensenesslax -7.907 8.742 7.396 -0.905
## positionphrase-final:sylposN 34.814 4.706 2315.958 7.399
## positionphrase-final:sylposC 53.826 4.706 2315.958 11.439
## positionphrase-final:tensenesslax -2.155 5.396 32.527 -0.399
## sylposN:tensenesslax -29.446 4.723 2315.958 -6.234
## sylposC:tensenesslax 15.801 4.723 2315.958 3.345
## positionphrase-final:sylposN:tensenesslax -16.700 6.637 2315.958 -2.516
## positionphrase-final:sylposC:tensenesslax 10.663 6.637 2315.958 1.606
## Pr(>|t|)
## (Intercept) 2.19e-08 ***
## positionphrase-final 0.048595 *
## sylposN 0.266632
## sylposC < 2e-16 ***
## tensenesslax 0.394228
## positionphrase-final:sylposN 1.92e-13 ***
## positionphrase-final:sylposC < 2e-16 ***
## positionphrase-final:tensenesslax 0.692178
## sylposN:tensenesslax 5.38e-10 ***
## sylposC:tensenesslax 0.000835 ***
## positionphrase-final:sylposN:tensenesslax 0.011938 *
## positionphrase-final:sylposC:tensenesslax 0.108316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- sylpsN sylpsC tnsnss pst-:N pst-:C pstn-: sylpN:
## pstnphrs-fn -0.018
## sylposN -0.240 0.349
## sylposC -0.240 0.349 0.500
## tensenesslx -0.627 -0.122 0.192 0.192
## pstnphrs-:N 0.171 -0.489 -0.713 -0.356 -0.137
## pstnphrs-:C 0.171 -0.489 -0.356 -0.713 -0.137 0.500
## pstnphrs-f: -0.136 -0.563 -0.311 -0.311 0.219 0.436 0.436
## sylpsN:tnsn 0.171 -0.248 -0.710 -0.355 -0.270 0.506 0.253 0.438
## sylpsC:tnsn 0.171 -0.248 -0.355 -0.710 -0.270 0.253 0.506 0.438 0.500
## pstnphr-:N: -0.121 0.347 0.505 0.253 0.192 -0.709 -0.354 -0.615 -0.712
## pstnphr-:C: -0.121 0.347 0.253 0.505 0.192 -0.354 -0.709 -0.615 -0.356
## sylpC: ps-:N:
## pstnphrs-fn
## sylposN
## sylposC
## tensenesslx
## pstnphrs-:N
## pstnphrs-:C
## pstnphrs-f:
## sylpsN:tnsn
## sylpsC:tnsn
## pstnphr-:N: -0.356
## pstnphr-:C: -0.712 0.500
## convergence code: 0
## boundary (singular) fit: see ?isSingular#models with additional random slopes do not converge
qqPlot(resid(m4))## [1] 1589 1890abline(h = cut<- 105)
mono2<-droplevels(subset(mono, resid(m4)<cut))
m4.1<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+position|subject) + (0+position|word), data = mono2)## boundary (singular) fit: see ?isSingular## Warning: Model failed to converge with 2 negative eigenvalues: -5.3e-03 -6.4e+01# after cut model m4.1 does not converge any more
mono2$position.num <- as.numeric(mono2$position)
mono2$tenseness.num <- as.numeric(mono2$tenseness)
mono2$sylpos.num <- as.numeric(mono2$sylpos)
m5<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+position+tenseness|subject) + (0+position|word), data = mono2)## boundary (singular) fit: see ?isSingularm5.r2dummy<-lmer(sylposdur ~ position.num * sylpos.num * tenseness.num +
(1+position.num+tenseness.num|subject) + (0+position.num|word), data = mono2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0120507 (tol = 0.002, component 1)r.squaredGLMM(m5.r2dummy)## Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.## R2m R2c
## [1,] 0.2914292 0.4106092qqPlot(resid(m5))
## [1] 2085 1895summary(m5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## word) + (0 + position + tenseness | subject) + (0 + position | word)
## Data: mono2
##
## REML criterion at convergence: 22891.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7908 -0.6844 -0.0448 0.5635 3.4316
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject positionphrase-medial 0.000e+00 0.000000
## positionphrase-final 6.994e+01 8.363073 NaN
## tensenesslax 2.130e+00 1.459623 NaN -1.00
## subject.1 (Intercept) 6.743e+01 8.211571
## word positionphrase-medial 1.091e+02 10.442869
## positionphrase-final 2.084e+02 14.434746 1.00
## word.1 (Intercept) 7.259e-06 0.002694
## Residual 1.025e+03 32.018253
## Number of obs: 2342, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 93.819 6.282 10.483 14.934
## positionphrase-final 10.561 4.658 29.651 2.267
## sylposN 6.319 3.286 2304.606 1.923
## sylposC -29.373 3.286 2304.606 -8.940
## tensenesslax -5.924 8.092 7.651 -0.732
## positionphrase-final:sylposN 34.143 4.608 2304.586 7.410
## positionphrase-final:sylposC 53.155 4.608 2304.587 11.536
## positionphrase-final:tensenesslax -2.728 5.399 28.616 -0.505
## sylposN:tensenesslax -31.468 4.619 2304.477 -6.813
## sylposC:tensenesslax 13.216 4.622 2304.498 2.859
## positionphrase-final:sylposN:tensenesslax -16.057 6.488 2304.481 -2.475
## positionphrase-final:sylposC:tensenesslax 11.868 6.491 2304.491 1.829
## Pr(>|t|)
## (Intercept) 2.11e-08 ***
## positionphrase-final 0.03083 *
## sylposN 0.05456 .
## sylposC < 2e-16 ***
## tensenesslax 0.48593
## positionphrase-final:sylposN 1.76e-13 ***
## positionphrase-final:sylposC < 2e-16 ***
## positionphrase-final:tensenesslax 0.61727
## sylposN:tensenesslax 1.22e-11 ***
## sylposC:tensenesslax 0.00428 **
## positionphrase-final:sylposN:tensenesslax 0.01340 *
## positionphrase-final:sylposC:tensenesslax 0.06760 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- sylpsN sylpsC tnsnss pst-:N pst-:C pstn-: sylpN:
## pstnphrs-fn 0.170
## sylposN -0.264 0.356
## sylposC -0.264 0.356 0.505
## tensenesslx -0.644 -0.164 0.205 0.205
## pstnphrs-:N 0.188 -0.499 -0.713 -0.360 -0.146
## pstnphrs-:C 0.188 -0.499 -0.360 -0.713 -0.146 0.505
## pstnphrs-f: -0.146 -0.585 -0.307 -0.307 0.231 0.431 0.431
## sylpsN:tnsn 0.188 -0.253 -0.711 -0.359 -0.287 0.507 0.256 0.431
## sylpsC:tnsn 0.188 -0.253 -0.359 -0.711 -0.287 0.256 0.507 0.430 0.503
## pstnphr-:N: -0.134 0.354 0.506 0.256 0.204 -0.710 -0.358 -0.604 -0.712
## pstnphr-:C: -0.134 0.354 0.256 0.506 0.204 -0.358 -0.710 -0.604 -0.358
## sylpC: ps-:N:
## pstnphrs-fn
## sylposN
## sylposC
## tensenesslx
## pstnphrs-:N
## pstnphrs-:C
## pstnphrs-f:
## sylpsN:tnsn
## sylpsC:tnsn
## pstnphr-:N: -0.358
## pstnphr-:C: -0.712 0.503
## convergence code: 0
## boundary (singular) fit: see ?isSingular# best model is m5
# model withouth stiel
no5<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+position+tenseness|subject) + (0+position|word), data = nostiel)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0103595 (tol = 0.002, component 1)plot(allEffects(no5))
plot(allEffects(m5))
summary(no5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## word) + (0 + position + tenseness | subject) + (0 + position | word)
## Data: nostiel
##
## REML criterion at convergence: 16191.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7503 -0.6303 -0.0323 0.6253 4.1803
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject positionphrase-medial 1.167e+02 10.80416
## positionphrase-final 7.859e+01 8.86530 0.45
## tensenesslax 3.377e+00 1.83767 -1.00 -0.48
## subject.1 (Intercept) 1.558e-04 0.01248
## word positionphrase-medial 2.856e+01 5.34437
## positionphrase-final 4.081e+01 6.38845 1.00
## word.1 (Intercept) 3.834e-03 0.06192
## Residual 5.557e+02 23.57381
## Number of obs: 1767, groups: subject, 10; word, 6
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 69.6699 5.0058 13.4724
## positionphrase-final 8.2600 4.3434 20.9189
## sylposN 36.2687 2.7782 1732.9493
## sylposC -2.6334 2.7782 1732.9493
## tensenesslax 0.1611 5.2041 6.3374
## positionphrase-final:sylposN 40.9826 3.8959 1732.9493
## positionphrase-final:sylposC 48.5343 3.8959 1732.9493
## positionphrase-final:tensenesslax -4.4745 3.9804 116.3282
## sylposN:tensenesslax -41.2723 3.9222 1732.9493
## sylposC:tensenesslax 8.4041 3.9222 1732.9493
## positionphrase-final:sylposN:tensenesslax -18.8357 5.4958 1732.9493
## positionphrase-final:sylposC:tensenesslax 16.6428 5.4958 1732.9493
## t value Pr(>|t|)
## (Intercept) 13.918 2.22e-09 ***
## positionphrase-final 1.902 0.071065 .
## sylposN 13.055 < 2e-16 ***
## sylposC -0.948 0.343324
## tensenesslax 0.031 0.976262
## positionphrase-final:sylposN 10.520 < 2e-16 ***
## positionphrase-final:sylposC 12.458 < 2e-16 ***
## positionphrase-final:tensenesslax -1.124 0.263270
## sylposN:tensenesslax -10.523 < 2e-16 ***
## sylposC:tensenesslax 2.143 0.032276 *
## positionphrase-final:sylposN:tensenesslax -3.427 0.000624 ***
## positionphrase-final:sylposC:tensenesslax 3.028 0.002496 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- sylpsN sylpsC tnsnss pst-:N pst-:C pstn-: sylpN:
## pstnphrs-fn -0.431
## sylposN -0.277 0.320
## sylposC -0.277 0.320 0.500
## tensenesslx -0.590 0.142 0.267 0.267
## pstnphrs-:N 0.198 -0.448 -0.713 -0.357 -0.190
## pstnphrs-:C 0.198 -0.448 -0.357 -0.713 -0.190 0.500
## pstnphrs-f: 0.101 -0.460 -0.349 -0.349 -0.192 0.489 0.489
## sylpsN:tnsn 0.197 -0.227 -0.708 -0.354 -0.377 0.505 0.253 0.493
## sylpsC:tnsn 0.197 -0.227 -0.354 -0.708 -0.377 0.253 0.505 0.493 0.500
## pstnphr-:N: -0.140 0.318 0.506 0.253 0.269 -0.709 -0.354 -0.690 -0.714
## pstnphr-:C: -0.140 0.318 0.253 0.506 0.269 -0.354 -0.709 -0.690 -0.357
## sylpC: ps-:N:
## pstnphrs-fn
## sylposN
## sylposC
## tensenesslx
## pstnphrs-:N
## pstnphrs-:C
## pstnphrs-f:
## sylpsN:tnsn
## sylpsC:tnsn
## pstnphr-:N: -0.357
## pstnphr-:C: -0.714 0.500
## convergence code: 0
## Model failed to converge with max|grad| = 0.0103595 (tol = 0.002, component 1)1.3 Overall lengthening for each syllable constituent
# Without tenseness group
mono %>% group_by(sylpos, position) %>%
summarise(mean.ms = mean(sylposdur)) %>%
spread(position, mean.ms) %>%
mutate(percent = `phrase-final` / `phrase-medial`) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'sylpos' (override with `.groups` argument)## # A tibble: 3 x 5
## # Groups: sylpos [3]
## sylpos medial final percent lengthening
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 O 92.3 101. 1.10 10 %
## 2 N 81.2 117. 1.44 44 %
## 3 C 68.3 136. 2.00 100 %# With tenseness group
mono %>% group_by(sylpos, position,tenseness) %>%
summarise(mean.ms = mean(sylposdur)) %>%
spread(position, mean.ms) %>%
mutate(percent = `phrase-final` / `phrase-medial`) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'sylpos', 'position' (override with `.groups` argument)## # A tibble: 6 x 6
## # Groups: sylpos [3]
## sylpos tenseness medial final percent lengthening
## <fct> <fct> <dbl> <dbl> <dbl> <chr>
## 1 O tense 96.2 106. 1.11 11 %
## 2 O lax 88.4 96.2 1.09 9 %
## 3 N tense 99.9 145. 1.45 45 %
## 4 N lax 62.7 88.6 1.41 41 %
## 5 C tense 64.2 128. 2.00 100 %
## 6 C lax 72.3 144. 2.00 100 %# normalized
mono %>% group_by(sylpos, position) %>%
summarise(mean.norm = mean(sylposdurnorm)) %>%
spread(position, mean.norm) %>%
mutate(percent = `phrase-final` / `phrase-medial`) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'sylpos' (override with `.groups` argument)## # A tibble: 3 x 5
## # Groups: sylpos [3]
## sylpos medial final percent lengthening
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 O 0.498 0.548 1.10 10 %
## 2 N 0.435 0.627 1.44 44 %
## 3 C 0.368 0.738 2.01 101 %# Disyllabic without tenseness
di %>% mutate(sylpos=factor(sylpos, levels = c("O1","N1","O2","N2"))) %>%
group_by(position, sylpos) %>%
summarise(mean.ms = mean(sylposdur)) %>%
spread(position, mean.ms) %>%
mutate(percent = `phrase-final` / `phrase-medial`) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'position' (override with `.groups` argument)## # A tibble: 4 x 5
## sylpos medial final percent lengthening
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 O1 72.6 78.2 1.08 8 %
## 2 N1 59.5 73.3 1.23 23 %
## 3 O2 80.1 98.4 1.23 23 %
## 4 N2 48.7 149. 3.06 206 %# Disyllabic with tenseness
di %>% mutate(sylpos=factor(sylpos, levels = c("O1","N1","O2","N2"))) %>%
group_by(position, sylpos,tenseness) %>%
summarise(mean.ms = mean(sylposdur)) %>%
spread(position, mean.ms) %>%
mutate(percent = `phrase-final` / `phrase-medial`) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'position', 'sylpos' (override with `.groups` argument)## # A tibble: 8 x 6
## # Groups: sylpos [4]
## sylpos tenseness medial final percent lengthening
## <fct> <fct> <dbl> <dbl> <dbl> <chr>
## 1 O1 tense 77.6 80.1 1.03 3 %
## 2 O1 lax 67.6 76.3 1.13 13 %
## 3 N1 tense 74.2 93.1 1.25 25 %
## 4 N1 lax 44.9 53.3 1.19 19 %
## 5 O2 tense 79.7 95.9 1.20 20 %
## 6 O2 lax 80.4 101. 1.26 26 %
## 7 N2 tense 44.0 147. 3.34 234 %
## 8 N2 lax 53.2 151. 2.83 183 %1.3.1 Excursus: Normalization
Normalization of dependent variable for speech rate does not change the effects considerably
m5.norm<-lmer(sylposdurnorm ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+position+sylpos|subject) + (0+position|word), data = mono)## boundary (singular) fit: see ?isSingularplot(allEffects(m5.norm))
plot(allEffects(m5))
1.3.2 Figure 2 (normalized stacked mean duration per tenseness and condition)
leng.perc<-mono %>% group_by(position,word, sylpos) %>%
summarise(mean.dur = mean(sylposdurnorm)) %>%
spread(position, mean.dur) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'position', 'word' (override with `.groups` argument)leng.perc.di<-di %>% mutate(sylpos=factor(sylpos, levels = c("O1","N1","O2","N2"))) %>%
group_by(position,word, sylpos) %>%
summarise(mean.dur = mean(sylposdurnorm)) %>%
spread(position, mean.dur) %>%
rename(medial = `phrase-medial` , final = `phrase-final`) %>%
mutate(lengthening = paste(round((final/medial-1)*100),"%"))## `summarise()` regrouping output by 'position', 'word' (override with `.groups` argument)### normalized duration in syllables (not in paper)
(plot.ac.mono<- mono %>% group_by(position,word,tenseness, sylpos) %>%
dplyr::summarise(mean.dur = mean(sylposdurnorm), sd.dur = sd(sylposdurnorm)) %>%
filter(position=="phrase-final") %>%
ungroup() %>%
mutate(lengthening = leng.perc$lengthening) %>%
group_by(word) %>%
mutate(ymin=cumsum(mean.dur) -sd.dur/2, ymax=cumsum(mean.dur) + sd.dur/2) %>%
ggplot(aes(x=word, y=mean.dur, fill=sylpos))+
geom_bar(
stat="identity",
position=position_stack(reverse=TRUE),alpha=0.9
)+
geom_errorbar(aes(ymin=ymin, ymax=ymax),
stat="identity",
width=.2 # Width of the error bars
)+
coord_flip()+
ylab("")+
geom_text(aes(x=word, y=mean.dur, label = lengthening),
color="black", hjust= 2,size = 3, position=position_stack(reverse=TRUE))+
annotate("text", x=1:8,y= 2.7,label=rep(c("tense","lax"),4),size=3)+
# ylim(0,2.7)+
# scale_fill_grey(start=0,end=0.6, name="")+
xlab("Monosyllabic targets")+
scale_fill_brewer(palette="Set3",name="",labels=c("Onset","Nucleus","Coda"))+
theme_bw(base_size = 10) +
theme( legend.position="top"))## `summarise()` regrouping output by 'position', 'word', 'tenseness' (override with `.groups` argument)
(plot.ac.di<-
di %>% mutate(sylpos=factor(sylpos, levels = c("O1","N1","O2","N2"))) %>%
group_by(position,word,tenseness, sylpos) %>%
summarise(mean.dur = mean(sylposdurnorm), sd.dur = sd(sylposdurnorm)) %>%
filter(position=="phrase-final") %>%
ungroup() %>%
mutate(lengthening = leng.perc.di$lengthening) %>%
group_by(word) %>%
mutate(ymin=cumsum(mean.dur) -sd.dur/2, ymax=cumsum(mean.dur) + sd.dur/2) %>%
ggplot(aes(x=word, y=mean.dur, fill=sylpos))+
geom_bar(
stat="identity",
position=position_stack(reverse=T),alpha=0.9
)+
geom_errorbar(aes(ymin=ymin, ymax=ymax),
stat="identity",
width=.2 # Width of the error bars
)+
coord_flip()+
ylab("Normalized duration in syllables")+
geom_text(aes(x=word, y=mean.dur, label = lengthening),
color="black", vjust= 0,hjust=2,size = 3, position=position_stack(reverse=T))+
annotate("text", x=1:4,y= 2.7,label=rep(c("tense","lax"),2),size=3)+
# scale_fill_grey(start=0,end=0.6, name="")+
xlab("Disyllabic targets")+
scale_fill_brewer(palette="Set2",name="",labels=c("Onset 1","Nucleus 1","Onset 2","Nucleus 2"))+
theme_bw(base_size = 10) +
theme( legend.position="top"))## `summarise()` regrouping output by 'position', 'word', 'tenseness' (override with `.groups` argument)
Figure 3: Acoustic mean duration of syllable constituents per phrasal position
# Duration in milliseconds
(plot.ac.mono<- mono %>% group_by(position,word,tenseness, sylpos) %>%
dplyr::summarise(mean.dur = mean(sylposdur), sd.dur = sd(sylposdur)) %>%
filter(position=="phrase-final") %>%
ungroup() %>%
mutate(lengthening = leng.perc$lengthening) %>%
group_by(word) %>%
mutate(ymin=cumsum(mean.dur) -sd.dur/2, ymax=cumsum(mean.dur) + sd.dur/2) %>%
ggplot(aes(x=word, y=mean.dur, fill=sylpos))+
geom_bar(
stat="identity",
position=position_stack(reverse=TRUE),alpha=0.9
)+
geom_errorbar(aes(ymin=ymin, ymax=ymax),
stat="identity",
width=.2 # Width of the error bars
)+
coord_flip()+
ylab("")+
geom_text(aes(x=word, y=mean.dur, label = lengthening),
color="black", hjust= 2,size = 3, position=position_stack(reverse=TRUE))+
annotate("text", x=1:8,y= 500,label=rep(c("tense","lax"),4),size=3)+
# ylim(0,2.7)+
# scale_fill_grey(start=0,end=0.6, name="")+
xlab("Monosyllabic targets")+
scale_fill_brewer(palette="Set3",name="",labels=c("Onset","Nucleus","Coda"))+
theme_bw(base_size = 10) +
theme( legend.position="top"))## `summarise()` regrouping output by 'position', 'word', 'tenseness' (override with `.groups` argument)
(plot.ac.di<-
di %>% mutate(sylpos=factor(sylpos, levels = c("O1","N1","O2","N2"))) %>%
group_by(position,word,tenseness, sylpos) %>%
summarise(mean.dur = mean(sylposdur), sd.dur = sd(sylposdur)) %>%
filter(position=="phrase-final") %>%
ungroup() %>%
mutate(lengthening = leng.perc.di$lengthening) %>%
group_by(word) %>%
mutate(ymin=cumsum(mean.dur) -sd.dur/2, ymax=cumsum(mean.dur) + sd.dur/2) %>%
ggplot(aes(x=word, y=mean.dur, fill=sylpos))+
geom_bar(
stat="identity",
position=position_stack(reverse=T),alpha=0.9
)+
geom_errorbar(aes(ymin=ymin, ymax=ymax),
stat="identity",
width=.2 # Width of the error bars
)+
coord_flip()+
ylab("Acoustic duration (ms)")+
geom_text(aes(x=word, y=mean.dur, label = lengthening),
color="black", vjust= 0,hjust=2,size = 3, position=position_stack(reverse=T))+
annotate("text", x=1:4,y= 500,label=rep(c("tense","lax"),2),size=3)+
# scale_fill_grey(start=0,end=0.6, name="")+
xlab("Disyllabic targets")+
scale_fill_brewer(palette="Set2",name="",labels=c("Onset 1","Nucleus 1","Onset 2","Nucleus 2"))+
theme_bw(base_size = 10) +
theme( legend.position="top"))## `summarise()` regrouping output by 'position', 'word', 'tenseness' (override with `.groups` argument)
ggpubr::ggarrange(plot.ac.mono, plot.ac.di, common.legend = F,labels = c("a)","b)"),
font.label = list(size=11,face="plain"),align = "h", widths = c(1.1,1), ncol = 1)
ggsave(file="v2.stackedacousticfinal.pdf", width = 200, height = 160, units = "mm")
# Plots of acoustic vowel length####
mono$wordpair <- mono$word
levels(mono$wordpair) <- c(rep("[a:] vs. [a]",2),
rep("[e:] vs. [ɛ]",2),
rep("[u:] vs. [ʊ]",2),
rep("[i:] vs. [ɪ]",2))
di$wordpair <- di$word
levels(di$wordpair) <- c(rep("[y:] vs. [ʏ]",2),rep("[i:] vs. [ɪ]",2))
(plot.ac.mono.data<-
ggplot(mono %>% filter(sylpos=="N"), aes(x=tenseness, y=sylposdur,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3)+facet_wrap(~wordpair,nrow=1)+
scale_fill_brewer(palette = "Set1")+
xlab("Tenseness")+
ylab("Acoustic duration (ms)")+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank())
)
(plot.ac.di.data<-ggplot(di %>% filter(sylpos=="N1"), aes(x=tenseness, y=sylposdur, fill=position))+
geom_boxplot(alpha=.3)+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
facet_wrap(~wordpair,nrow=1)+
scale_fill_brewer(palette = "Set1")+
xlab("Tenseness")+
ylab("")+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank())
)
cairo_pdf(filename = "v2.ac-vowel-tenseness-position.pdf",width=8,height=5)
ggpubr::ggarrange(plot.ac.mono.data, plot.ac.di.data, common.legend = T,labels = c("a)","b)"),
font.label = list(size=11,face="plain"),align = "h", widths = c(1.1,1))
dev.off()## png
## 21.3.3 Subset model acoustic monosyllabic for lax only
mono.lax<-droplevels(subset(mono, tenseness=="lax"))
mono.lax.nucl<-droplevels(subset(mono, tenseness=="lax" & sylpos=="N"))
mono.tense<-droplevels(subset(mono, tenseness=="tense"))
mono.tense.nucl<-droplevels(subset(mono, tenseness=="tense" & sylpos=="N"))
l1<-lmer(sylposdur ~ position + sylpos + (1|subject) + (1|word), data = mono.lax)
l2<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word), data = mono.lax)
anova(l1,l2)## refitting model(s) with ML (instead of REML)## Data: mono.lax
## Models:
## l1: sylposdur ~ position + sylpos + (1 | subject) + (1 | word)
## l2: sylposdur ~ position * sylpos + (1 | subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## l1 7 11590 11626 -5788.1 11576
## l2 9 11359 11405 -5670.4 11341 235.22 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1l3<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = mono.lax)## boundary (singular) fit: see ?isSingularanova(l2,l3,refit=F) #better## Data: mono.lax
## Models:
## l2: sylposdur ~ position * sylpos + (1 | subject) + (1 | word)
## l3: sylposdur ~ position * sylpos + (1 | subject) + (1 | word) +
## l3: (0 + position + sylpos | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## l2 9 11335 11381 -5658.7 11317
## l3 19 10448 10544 -5204.8 10410 907.61 10 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1qqPlot(resid(l3))## [1] 844 530abline(h = cut <- 50)
mono.lax.sub<-droplevels(subset(mono.lax, resid(l3) < cut))
l4<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = mono.lax.sub)## boundary (singular) fit: see ?isSingularqqPlot(resid(l4))## [1] 1155 692abline(h = cut <- -50)
mono.lax.sub<-droplevels(subset(mono.lax.sub, resid(l4) > cut))
l5<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = mono.lax.sub)## boundary (singular) fit: see ?isSingularqqPlot(resid(l5))
## [1] 560 797plot(allEffects(l5))
summary(l5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos + (1 | subject) + (1 | word) +
## (0 + position + sylpos | word)
## Data: mono.lax.sub
##
## REML criterion at convergence: 9859.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89003 -0.65202 -0.01035 0.59712 3.05073
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 5.174e+01 7.193e+00
## word (Intercept) 3.886e-07 6.234e-04
## word.1 positionphrase-medial 1.741e+03 4.172e+01
## positionphrase-final 2.228e+03 4.720e+01 1.00
## sylposN 2.795e+03 5.287e+01 -0.98 -0.98
## sylposC 2.400e+03 4.899e+01 -1.00 -1.00 0.98
## Residual 2.872e+02 1.695e+01
## Number of obs: 1155, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 86.896 21.020 3.062 4.134 0.0247 *
## positionphrase-final 9.554 3.343 4.429 2.858 0.0408 *
## sylposN -23.989 26.492 2.997 -0.906 0.4320
## sylposC -14.912 24.555 2.999 -0.607 0.5866
## positionphrase-final:sylposN 15.762 2.425 1131.024 6.500 1.2e-10 ***
## positionphrase-final:sylposC 60.525 2.459 1131.316 24.612 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- sylpsN sylpsC pst-:N
## pstnphrs-fn 0.787
## sylposN -0.972 -0.790
## sylposC -0.991 -0.777 0.974
## pstnphrs-:N 0.029 -0.365 -0.046 -0.025
## pstnphrs-:C 0.029 -0.360 -0.023 -0.049 0.496
## convergence code: 0
## boundary (singular) fit: see ?isSingularlattice::dotplot(ranef(l5))## $subject
##
## $word
1.3.4 Proportional lengthening of vowels in nuclei, monosyllabic, with and without Stiel/still
tn1<-lmer(sylposdur ~ position + (1|subject) + (0+position|word), data = mono.tense.nucl)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0297967 (tol = 0.002, component 1)qqPlot(resid(tn1))
## [1] 254 278summary(tn1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position + (1 | subject) + (0 + position | word)
## Data: mono.tense.nucl
##
## REML criterion at convergence: 3464.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5604 -0.6538 -0.1107 0.6794 3.1496
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 315.1 17.75
## word positionphrase-medial 664.3 25.77
## positionphrase-final 1190.7 34.51 0.99
## Residual 379.0 19.47
## Number of obs: 390, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 100.508 14.127 4.191 7.115 0.00173 **
## positionphrase-final 43.895 5.043 3.011 8.704 0.00315 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn 0.745
## convergence code: 0
## Model failed to converge with max|grad| = 0.0297967 (tol = 0.002, component 1)tn1.coef<-summary(tn1)$coefficients
# Proportion lengthening of tense vowel with Stiel
(tn1.coef[1,1]+tn1.coef[2,1])/tn1.coef[1,1] # tense with Stiel## [1] 1.436735# Model of proportional lengthening in monosyllabic nuclei
mono.proplen <- mono %>%
filter(sylpos=="N") %>%
group_by(subject,tenseness,wordpair,position) %>%
summarise(mean = mean(sylposdur)) %>%
tidyr::spread(position, mean) %>%
mutate(proplen = ((`phrase-final`/`phrase-medial`)-1)*100)## `summarise()` regrouping output by 'subject', 'tenseness', 'wordpair' (override with `.groups` argument)mp1 <- lmer(proplen ~ tenseness + (1|subject) + (1|wordpair), data=mono.proplen)
qqPlot(resid(mp1))
## [1] 80 52summary(mp1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: proplen ~ tenseness + (1 | subject) + (1 | wordpair)
## Data: mono.proplen
##
## REML criterion at convergence: 722.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.85764 -0.55990 -0.06001 0.50219 2.36290
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 151.05 12.290
## wordpair (Intercept) 37.99 6.164
## Residual 466.85 21.607
## Number of obs: 80, groups: subject, 10; wordpair, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.268 6.023 10.353 8.014 9.37e-06 ***
## tensenesslax -5.756 4.831 66.000 -1.191 0.238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## tensenesslx -0.401plot(allEffects(mp1))
# lax only
ln1<-lmer(sylposdur ~ position + (1|subject) + (1|word), data = mono.lax.nucl)
qqPlot(resid(ln1))## [1] 328 33abline(h = cut <- c(-40,40))
mono.lax.nucl.sub<-droplevels(subset(mono.lax.nucl, resid(ln1) > cut[1] & resid(ln1) < cut[2]))
ln2<-lmer(sylposdur ~ position + (1|subject) + (1|word), data = mono.lax.nucl.sub)
qqPlot(resid(ln2))
## [1] 387 36summary(ln2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position + (1 | subject) + (1 | word)
## Data: mono.lax.nucl.sub
##
## REML criterion at convergence: 3063.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1331 -0.5632 -0.0549 0.5202 3.3720
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 127.1 11.27
## word (Intercept) 160.4 12.66
## Residual 138.4 11.76
## Number of obs: 389, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.931 7.315 5.075 8.603 0.000325 ***
## positionphrase-final 25.759 1.195 375.053 21.559 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn -0.082ln2.coef<-summary(ln2)$coefficients
# Proportion lengthening of lax vowels with Stiel
(ln2.coef[1,1]+ln2.coef[2,1])/ln2.coef[1,1]## [1] 1.409323#without Stiel/stiel tense
mono.tense.nucl.nostiel<-droplevels(subset(mono, tenseness=="tense" & sylpos=="N" & !(word%in%c("Stiel","still")) ))
mono.lax.nucl.nostiel<-droplevels(subset(mono, tenseness=="lax" & sylpos=="N" & !(word%in%c("Stiel","still")) ))
#tense
tn.nostiel<-lmer(sylposdur ~ position + (1|subject) + (0+position|word), data = mono.tense.nucl.nostiel)## boundary (singular) fit: see ?isSingularqqPlot(resid(tn.nostiel))
## [1] 196 208summary(tn.nostiel)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position + (1 | subject) + (0 + position | word)
## Data: mono.tense.nucl.nostiel
##
## REML criterion at convergence: 2592.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6745 -0.6306 -0.1063 0.6545 3.1057
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 367.6 19.17
## word positionphrase-medial 798.6 28.26
## positionphrase-final 1154.6 33.98 1.00
## Residual 362.8 19.05
## Number of obs: 293, groups: subject, 10; word, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 106.437 17.478 2.573 6.09 0.01366 *
## positionphrase-final 48.310 3.984 2.211 12.13 0.00459 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn 0.738
## convergence code: 0
## boundary (singular) fit: see ?isSingulartn1.coef.nostiel<-summary(tn.nostiel)$coefficients
# Proportion lengthening of tense vowel without Stiel
(tn1.coef.nostiel[1,1]+tn1.coef.nostiel[2,1])/tn1.coef.nostiel[1,1]## [1] 1.45389#lax
ln.nostiel<-lmer(sylposdur ~ position + (1|subject) + (0+position|word), data = mono.lax.nucl.nostiel)## boundary (singular) fit: see ?isSingularqqPlot(resid(ln.nostiel))
## [1] 254 220summary(ln.nostiel)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position + (1 | subject) + (0 + position | word)
## Data: mono.lax.nucl.nostiel
##
## REML criterion at convergence: 2276.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0992 -0.5366 -0.0712 0.5662 2.9576
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 118.0 10.86
## word positionphrase-medial 229.1 15.14
## positionphrase-final 202.7 14.24 1.00
## Residual 113.2 10.64
## Number of obs: 296, groups: subject, 10; word, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 65.258 9.432 2.644 6.919 0.0092 **
## positionphrase-final 25.298 1.344 7.053 18.816 2.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn -0.420
## convergence code: 0
## boundary (singular) fit: see ?isSingularln1.coef.nostiel<-summary(ln.nostiel)$coefficients
# Proportion lengthening of lax vowel without Stiel
(ln1.coef.nostiel[1,1]+ln1.coef.nostiel[2,1])/ln1.coef.nostiel[1,1]## [1] 1.387658l2<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word), data = mono.lax)
anova(l1,l2)## refitting model(s) with ML (instead of REML)## Data: mono.lax
## Models:
## l1: sylposdur ~ position + sylpos + (1 | subject) + (1 | word)
## l2: sylposdur ~ position * sylpos + (1 | subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## l1 7 11590 11626 -5788.1 11576
## l2 9 11359 11405 -5670.4 11341 235.22 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1l3<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = mono.lax)## boundary (singular) fit: see ?isSingularanova(l2,l3,refit=F) #better## Data: mono.lax
## Models:
## l2: sylposdur ~ position * sylpos + (1 | subject) + (1 | word)
## l3: sylposdur ~ position * sylpos + (1 | subject) + (1 | word) +
## l3: (0 + position + sylpos | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## l2 9 11335 11381 -5658.7 11317
## l3 19 10448 10544 -5204.8 10410 907.61 10 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1qqPlot(resid(l3))## [1] 844 530abline(h = cut <- 50)
mono.lax.sub<-droplevels(subset(mono.lax, resid(l3) < cut))
l4<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = mono.lax.sub)## boundary (singular) fit: see ?isSingularqqPlot(resid(l4))## [1] 1155 692abline(h = cut <- -50)
mono.lax.sub<-droplevels(subset(mono.lax.sub, resid(l4) > cut))
l5<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = mono.lax.sub)## boundary (singular) fit: see ?isSingularqqPlot(resid(l5))
## [1] 560 797plot(allEffects(l5))
summary(l5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos + (1 | subject) + (1 | word) +
## (0 + position + sylpos | word)
## Data: mono.lax.sub
##
## REML criterion at convergence: 9859.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89003 -0.65202 -0.01035 0.59712 3.05073
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 5.174e+01 7.193e+00
## word (Intercept) 3.886e-07 6.234e-04
## word.1 positionphrase-medial 1.741e+03 4.172e+01
## positionphrase-final 2.228e+03 4.720e+01 1.00
## sylposN 2.795e+03 5.287e+01 -0.98 -0.98
## sylposC 2.400e+03 4.899e+01 -1.00 -1.00 0.98
## Residual 2.872e+02 1.695e+01
## Number of obs: 1155, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 86.896 21.020 3.062 4.134 0.0247 *
## positionphrase-final 9.554 3.343 4.429 2.858 0.0408 *
## sylposN -23.989 26.492 2.997 -0.906 0.4320
## sylposC -14.912 24.555 2.999 -0.607 0.5866
## positionphrase-final:sylposN 15.762 2.425 1131.024 6.500 1.2e-10 ***
## positionphrase-final:sylposC 60.525 2.459 1131.316 24.612 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- sylpsN sylpsC pst-:N
## pstnphrs-fn 0.787
## sylposN -0.972 -0.790
## sylposC -0.991 -0.777 0.974
## pstnphrs-:N 0.029 -0.365 -0.046 -0.025
## pstnphrs-:C 0.029 -0.360 -0.023 -0.049 0.496
## convergence code: 0
## boundary (singular) fit: see ?isSingularlattice::dotplot(ranef(l5))## $subject
##
## $word
# Model of proportional lengthening in disyllabic nuclei
di.proplen <- di %>%
filter(sylpos=="N1") %>%
group_by(subject,tenseness,wordpair,position) %>%
summarise(mean = mean(sylposdur)) %>%
tidyr::spread(position, mean) %>%
mutate(proplen = ((`phrase-final`/`phrase-medial`)-1)*100)## `summarise()` regrouping output by 'subject', 'tenseness', 'wordpair' (override with `.groups` argument)dp1 <- lmer(proplen ~ tenseness + (1|subject) + (1|wordpair), data=di.proplen)## boundary (singular) fit: see ?isSingularqqPlot(resid(dp1))
## [1] 21 32summary(dp1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: proplen ~ tenseness + (1 | subject) + (1 | wordpair)
## Data: di.proplen
##
## REML criterion at convergence: 351.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3945 -0.7795 -0.1186 0.6708 3.0899
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0 0.00
## wordpair (Intercept) 0.0 0.00
## Residual 521.8 22.84
## Number of obs: 40, groups: subject, 10; wordpair, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 29.847 5.108 38.000 5.843 9.34e-07 ***
## tensenesslax -6.635 7.224 38.000 -0.918 0.364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## tensenesslx -0.707
## convergence code: 0
## boundary (singular) fit: see ?isSingularplot(allEffects(dp1))
# Table with proplen
x<-capture.output(
texreg::texreg(list(texreg::extract(mp1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(dp1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
# texreg::extract(mf3.1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
),
digits = 1,booktabs = T,custom.model.names = c("Monosyllabic \\%","Disyllabic \\%"),
float.pos = "!htbp", label = "tab:proplen", use.packages = F,single.row = T,caption.above = T,
caption="Model predictions for the proportional lengthening of vowels in mono- and disyllabic words
(standards error in parentheses) for the fixed effects tenseness (tense vs. lax) and random intercepts for subjects and word pairs.",
dcolumn = T, fontsize = "scriptsize",center=T)
)
x[5]<-paste0(x[5], "\\centering")
x[16]<-paste0(x[16],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(mp1)[1],2),"/",round(r.squaredGLMM(mp1)[2],2),
"&", round(r.squaredGLMM(dp1)[1],2), "/", round(r.squaredGLMM(dp1)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{Model predictions for the proportional lengthening of vowels in mono- and disyllabic words
## (standards error in parentheses) for the fixed effects tenseness (tense vs. lax) and random intercepts for subjects and word pairs.}
## \begin{center}\centering
## \begin{scriptsize}
## \begin{tabular}{l D{)}{)}{9)3} D{)}{)}{9)3} }
## \toprule
## & \multicolumn{1}{c}{Monosyllabic \%} & \multicolumn{1}{c}{Disyllabic \%} \\
## \midrule
## (Intercept) & 48.3 \; (6.0)^{***} & 29.8 \; (5.1)^{***} \\
## tensenesslax & -5.8 \; (4.8) & -6.6 \; (7.2) \\
## \midrule
## AIC & 732.5 & 361.6 \\
## Num. obs. & 80 & 40 \\
## Num. groups: subject & 10 & 10 \\\midrule R$_m^2$/R$_c^2$ & 0.01/0.3&0.02/0.02\\
##
## Num. groups: wordpair & 4 & 2 \\
## \bottomrule
## \multicolumn{3}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{scriptsize}
## \label{tab:proplen}
## \end{center}
## \end{table}1.3.5 Post-hoc tests (multiple comparisons, Tukey-corrected) for mono, lax
l5em <- emmeans(l5, pairwise~position*sylpos)
l5em.tab<-l5em$contrasts %>%
summary(infer = T)
l5em.tab$p.value<-lapply(l5em.tab$p.value, FUN = function (x) {
# levels of p-values
if (x>=0.05) p <- "n.\\,s." # not significant
if (x<0.05) p <- "<0,05" # significant
if (x<=0.01) p <- "<0,01" # significant
if (x<=0.001) p <- "<0,001" # significant
return(p)
})
l5em.tab<-as.data.frame(l5em.tab)
colnames(l5em.tab)<-c("Comparison","$\\beta$","s","df","Lower CI","Upper CI","t","p")
l5em.tab.sig<-subset(l5em.tab, p!="n.\\,s.")
l5em.tab.sig## Comparison $\\beta$ s df Lower CI
## 10 phrase-medial,N - phrase-final,N -25.31610 3.338346 4.386355 -40.42224
## 12 phrase-medial,N - phrase-final,C -79.15585 7.598238 3.056582 -121.75405
## 14 phrase-final,N - phrase-final,C -53.83975 6.087517 3.267556 -86.42379
## 15 phrase-medial,C - phrase-final,C -70.07870 3.363707 4.517218 -85.08832
## Upper CI t p
## 10 -10.20995 -7.583425 <0,01
## 12 -36.55764 -10.417659 <0,01
## 14 -21.25571 -8.844288 <0,05
## 15 -55.06908 -20.833769 <0,0011.3.6 Monosyllabic Effect plots
Preparation
ef1b<-as.data.frame(effect("position*sylpos*tenseness",m5))
ef1b$sylpos<-factor(ef1b$sylpos, levels=c("O","N","C"))
ef1b$position<-factor(ef1b$position, levels=c("phrase-medial","phrase-final"))
ef1b$tenseness<-factor(ef1b$tenseness, levels=c("tense","lax"))
ef1b$pos.tense<-paste0(ef1b$tenseness," ",ef1b$position)
ef1b$pos.tense<-as.factor(ef1b$pos.tense)
(ef1b.plot<-
ef1b %>% ggplot(aes(x=sylpos, y=fit,col=position,group=position,lty=position)) +
geom_point() +
geom_line()+
# geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=.8) +
facet_grid(~tenseness)+
xlab("Syllable position")+
scale_color_brewer(palette = "Set1")+
ylab("Fit for duration (ms)")+
ylim(30,160)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef1b.plot.pos.tens<-
ef1b %>% ggplot(aes(x=sylpos, y=fit,col=pos.tense,group=pos.tense,lty=tenseness)) +
geom_point() +
geom_line()+
# geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=.8) +
# facet_grid(~tenseness)+
xlab("Syllable position")+
scale_color_brewer(palette = "Set1")+
ylab("Fit for duration (ms)")+
ylim(30,160)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
1.4 Disyllabic target words
#### Disyllabic acoustic target words ####
d1<-lmer(sylposdur ~ position + sylpos + tenseness + (1|subject) + (1|word), data = di)## boundary (singular) fit: see ?isSingularqqPlot(resid(d1))
## [1] 1402 1097d2<-lmer(sylposdur ~ position * sylpos + tenseness + (1|subject) + (1|word), data = di)## boundary (singular) fit: see ?isSingularanova(d1,d2)## refitting model(s) with ML (instead of REML)## Data: di
## Models:
## d1: sylposdur ~ position + sylpos + tenseness + (1 | subject) + (1 |
## d1: word)
## d2: sylposdur ~ position * sylpos + tenseness + (1 | subject) + (1 |
## d2: word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## d1 9 15024 15072 -7502.9 15006
## d2 12 14123 14188 -7049.6 14099 906.56 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1d3<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word), data = di)## boundary (singular) fit: see ?isSingularanova(d2,d3)## refitting model(s) with ML (instead of REML)## Data: di
## Models:
## d2: sylposdur ~ position * sylpos + tenseness + (1 | subject) + (1 |
## d2: word)
## d3: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## d3: word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## d2 12 14123 14188 -7049.6 14099
## d3 19 13889 13991 -6925.7 13851 247.84 7 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1d4<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+tenseness|subject) + (0+position+tenseness+sylpos|word), data = di)## boundary (singular) fit: see ?isSingular## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00anova(d3,d4,refit=F)## Data: di
## Models:
## d3: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## d3: word)
## d4: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## d4: word) + (0 + tenseness | subject) + (0 + position + tenseness +
## d4: sylpos | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## d3 19 13834 13936 -6898.1 13796
## d4 43 13681 13911 -6797.4 13595 201.36 24 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#best model is d4, models with additional random slopes do not converge
qqPlot(resid(d4))## [1] 1402 731abline(h = cut<- c(-50,60))
di2<-droplevels(subset(di, resid(d4)<cut[2] & resid(d4)> cut[1]))
di2$sylpos <- factor(di2$sylpos, levels = c("O1","N1","O2","N2"))
d5<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+tenseness|subject) + (0+position+tenseness+sylpos|word), data = di2)## boundary (singular) fit: see ?isSingular## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e-01d5.r2dummy<-lmer(sylposdur ~ position * sylpos * tenseness +
(1+tenseness|subject) + (1+position+tenseness+sylpos|word), data = di2)## boundary (singular) fit: see ?isSingularanova(d5, d5.r2dummy, refit=F)## Data: di2
## Models:
## d5.r2dummy: sylposdur ~ position * sylpos * tenseness + (1 + tenseness |
## d5.r2dummy: subject) + (1 + position + tenseness + sylpos | word)
## d5: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## d5: word) + (0 + tenseness | subject) + (0 + position + tenseness +
## d5: sylpos | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## d5.r2dummy 41 12854 13073 -6385.9 12772
## d5 43 12858 13088 -6385.9 12772 0 2 1# after cut model does not converge any more
qqPlot(resid(d5))
## [1] 773 335summary(d5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## word) + (0 + tenseness | subject) + (0 + position + tenseness +
## sylpos | word)
## Data: di2
##
## REML criterion at convergence: 12771.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5127 -0.6642 -0.0445 0.5997 3.9051
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject tensenesstense 6.551e+01 8.094e+00
## tensenesslax 3.947e+01 6.283e+00 0.96
## subject.1 (Intercept) 3.400e+00 1.844e+00
## word positionphrase-medial 3.967e+02 1.992e+01
## positionphrase-final 2.968e+02 1.723e+01 1.00
## tensenesslax 1.775e-01 4.213e-01 -1.00 -1.00
## sylposN1 7.060e+02 2.657e+01 -1.00 -1.00 1.00
## sylposO2 3.043e+02 1.745e+01 -1.00 -1.00 1.00 1.00
## sylposN2 6.696e+02 2.588e+01 -1.00 -1.00 1.00 1.00
## word.1 (Intercept) 5.103e-07 7.144e-04
## Residual 2.346e+02 1.532e+01
##
##
##
##
##
##
##
##
##
## 1.00
##
##
## Number of obs: 1541, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 75.7441 14.4127 1.8835
## positionphrase-final 4.6753 2.9112 5.8053
## sylposN1 -1.6831 18.9173 1.7739
## sylposO2 3.7672 12.5325 1.8074
## sylposN2 -31.9499 18.4312 1.7758
## tensenesslax -7.7560 19.8502 1.7714
## positionphrase-final:sylposN1 14.4965 3.1029 1502.9286
## positionphrase-final:sylposO2 11.8324 3.1028 1502.8700
## positionphrase-final:sylposN2 99.9974 3.1414 1503.6649
## positionphrase-final:tensenesslax 3.4240 4.1106 5.7696
## sylposN1:tensenesslax -21.3373 26.7512 1.7734
## sylposO2:tensenesslax 8.7856 17.7208 1.8062
## sylposN2:tensenesslax 15.2809 26.0637 1.7752
## positionphrase-final:sylposN1:tensenesslax -14.1171 4.3823 1502.9774
## positionphrase-final:sylposO2:tensenesslax 0.4459 4.3820 1502.8573
## positionphrase-final:sylposN2:tensenesslax -11.1218 4.4619 1505.2441
## t value Pr(>|t|)
## (Intercept) 5.255 0.038955 *
## positionphrase-final 1.606 0.161052
## sylposN1 -0.089 0.938113
## sylposO2 0.301 0.794730
## sylposN2 -1.733 0.240638
## tensenesslax -0.391 0.737913
## positionphrase-final:sylposN1 4.672 3.25e-06 ***
## positionphrase-final:sylposO2 3.813 0.000143 ***
## positionphrase-final:sylposN2 31.832 < 2e-16 ***
## positionphrase-final:tensenesslax 0.833 0.437985
## sylposN1:tensenesslax -0.798 0.517820
## sylposO2:tensenesslax 0.496 0.673740
## sylposN2:tensenesslax 0.586 0.623506
## positionphrase-final:sylposN1:tensenesslax -3.221 0.001303 **
## positionphrase-final:sylposO2:tensenesslax 0.102 0.918966
## positionphrase-final:sylposN2:tensenesslax -2.493 0.012788 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## convergence code: 0
## boundary (singular) fit: see ?isSingular1.4.1 Excursus: Normalization
Normalization of dependent variable for speech rate does not change the effects considerably
d5.norm<-lmer(sylposdur ~ position * sylpos * tenseness + (1|subject) + (1|word) +
(0+tenseness|subject) + (0+position+tenseness+sylpos|word), data = di2)## boundary (singular) fit: see ?isSingular## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e-01plot(allEffects(d5.norm))
summary(d5.norm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos * tenseness + (1 | subject) + (1 |
## word) + (0 + tenseness | subject) + (0 + position + tenseness +
## sylpos | word)
## Data: di2
##
## REML criterion at convergence: 12771.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5127 -0.6642 -0.0445 0.5997 3.9051
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject tensenesstense 6.551e+01 8.094e+00
## tensenesslax 3.947e+01 6.283e+00 0.96
## subject.1 (Intercept) 3.400e+00 1.844e+00
## word positionphrase-medial 3.967e+02 1.992e+01
## positionphrase-final 2.968e+02 1.723e+01 1.00
## tensenesslax 1.775e-01 4.213e-01 -1.00 -1.00
## sylposN1 7.060e+02 2.657e+01 -1.00 -1.00 1.00
## sylposO2 3.043e+02 1.745e+01 -1.00 -1.00 1.00 1.00
## sylposN2 6.696e+02 2.588e+01 -1.00 -1.00 1.00 1.00
## word.1 (Intercept) 5.103e-07 7.144e-04
## Residual 2.346e+02 1.532e+01
##
##
##
##
##
##
##
##
##
## 1.00
##
##
## Number of obs: 1541, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 75.7441 14.4127 1.8835
## positionphrase-final 4.6753 2.9112 5.8053
## sylposN1 -1.6831 18.9173 1.7739
## sylposO2 3.7672 12.5325 1.8074
## sylposN2 -31.9499 18.4312 1.7758
## tensenesslax -7.7560 19.8502 1.7714
## positionphrase-final:sylposN1 14.4965 3.1029 1502.9286
## positionphrase-final:sylposO2 11.8324 3.1028 1502.8700
## positionphrase-final:sylposN2 99.9974 3.1414 1503.6649
## positionphrase-final:tensenesslax 3.4240 4.1106 5.7696
## sylposN1:tensenesslax -21.3373 26.7512 1.7734
## sylposO2:tensenesslax 8.7856 17.7208 1.8062
## sylposN2:tensenesslax 15.2809 26.0637 1.7752
## positionphrase-final:sylposN1:tensenesslax -14.1171 4.3823 1502.9774
## positionphrase-final:sylposO2:tensenesslax 0.4459 4.3820 1502.8573
## positionphrase-final:sylposN2:tensenesslax -11.1218 4.4619 1505.2441
## t value Pr(>|t|)
## (Intercept) 5.255 0.038955 *
## positionphrase-final 1.606 0.161052
## sylposN1 -0.089 0.938113
## sylposO2 0.301 0.794730
## sylposN2 -1.733 0.240638
## tensenesslax -0.391 0.737913
## positionphrase-final:sylposN1 4.672 3.25e-06 ***
## positionphrase-final:sylposO2 3.813 0.000143 ***
## positionphrase-final:sylposN2 31.832 < 2e-16 ***
## positionphrase-final:tensenesslax 0.833 0.437985
## sylposN1:tensenesslax -0.798 0.517820
## sylposO2:tensenesslax 0.496 0.673740
## sylposN2:tensenesslax 0.586 0.623506
## positionphrase-final:sylposN1:tensenesslax -3.221 0.001303 **
## positionphrase-final:sylposO2:tensenesslax 0.102 0.918966
## positionphrase-final:sylposN2:tensenesslax -2.493 0.012788 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## convergence code: 0
## boundary (singular) fit: see ?isSingular###1.4.2 Post-hoc tests (multiple comparisons, Tukey-corrected):
d5em <- emmeans(d5, pairwise~position*sylpos*tenseness)
d5em.tab<-d5em$contrasts %>%
summary(infer = T)
d5em.tab$p.value<-lapply(d5em.tab$p.value, FUN = function (x) {
# levels of p-values
if (x>=0.05) p <- "n.\\,s." # not significant
if (x<0.05) p <- "<0.05" # significant
if (x<=0.01) p <- "<0.01" # significant
if (x<=0.001) p <- "<0.001" # significant
return(p)
})
d5em.tab<-as.data.frame(d5em.tab)
colnames(d5em.tab)<-c("Comparison","$\\beta$","s","df","Lower CI","Upper CI","t","p")
d5em.tab.sig<-subset(d5em.tab, p!="n.\\,s.")
rownames(d5em.tab.sig)<-seq(1, nrow(d5em.tab.sig), 1)1.4.3 Subset model acoustic disyllabic for lax only
di.lax<-droplevels(subset(di, tenseness=="lax"))
dl1<-lmer(sylposdur ~ position + sylpos + (1|subject) + (1|word), data = di.lax)## boundary (singular) fit: see ?isSingulardl2<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word), data = di.lax)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00259991 (tol = 0.002, component 1)anova(dl1,dl2)## refitting model(s) with ML (instead of REML)## Data: di.lax
## Models:
## dl1: sylposdur ~ position + sylpos + (1 | subject) + (1 | word)
## dl2: sylposdur ~ position * sylpos + (1 | subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dl1 8 7448.1 7485.4 -3716.1 7432.1
## dl2 11 6968.6 7019.9 -3473.3 6946.6 485.49 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dl3<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = di.lax)## boundary (singular) fit: see ?isSingularanova(dl2,dl3,refit=F) #better## Data: di.lax
## Models:
## dl2: sylposdur ~ position * sylpos + (1 | subject) + (1 | word)
## dl3: sylposdur ~ position * sylpos + (1 | subject) + (1 | word) +
## dl3: (0 + position + sylpos | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dl2 11 6940.1 6991.4 -3459.1 6918.1
## dl3 26 6895.5 7016.8 -3421.7 6843.5 74.614 15 6.649e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1qqPlot(resid(l3))## [1] 844 530abline(h = cut <- 50)
di.lax.sub<-droplevels(subset(di.lax, resid(dl3) < cut))
dl4<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position+sylpos|word), data = di.lax.sub)## boundary (singular) fit: see ?isSingular## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+00qqPlot(resid(dl4))## [1] 406 438abline(h = cut <- -50)
di.lax.sub<-droplevels(subset(di.lax.sub, resid(dl4) > cut))
dl5<-lmer(sylposdur ~ position * sylpos + (1|subject) + (1|word) + (0+position|word), data = di.lax.sub)## boundary (singular) fit: see ?isSingularqqPlot(resid(dl5))
## [1] 388 392plot(allEffects(dl5))
summary(dl5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: sylposdur ~ position * sylpos + (1 | subject) + (1 | word) +
## (0 + position | word)
## Data: di.lax.sub
##
## REML criterion at convergence: 6437.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5433 -0.6713 0.0178 0.6228 3.2631
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 43.9314 6.628
## word (Intercept) 0.0000 0.000
## word.1 positionphrase-medial 1.2255 1.107
## positionphrase-final 0.2851 0.534 -1.00
## Residual 254.8157 15.963
## Number of obs: 769, groups: subject, 10; word, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 45.0320 2.7581 14.6886 16.327 8.04e-11 ***
## positionphrase-final 8.3210 2.5592 6.3222 3.251 0.016186 *
## sylposN2 6.3133 2.2864 751.0147 2.761 0.005898 **
## sylposO1 22.6979 2.2804 751.0022 9.953 < 2e-16 ***
## sylposO2 35.4649 2.2804 751.0022 15.552 < 2e-16 ***
## positionphrase-final:sylposN2 86.5637 3.2979 751.1636 26.248 < 2e-16 ***
## positionphrase-final:sylposO1 0.2655 3.2250 751.0022 0.082 0.934405
## positionphrase-final:sylposO2 12.1154 3.2250 751.0022 3.757 0.000185 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- sylpN2 sylpO1 sylpO2 ps-:N2 ps-:O1
## pstnphrs-fn -0.497
## sylposN2 -0.412 0.444
## sylposO1 -0.413 0.446 0.499
## sylposO2 -0.413 0.446 0.499 0.500
## pstnphr-:N2 0.286 -0.616 -0.693 -0.346 -0.346
## pstnphr-:O1 0.292 -0.630 -0.353 -0.707 -0.354 0.489
## pstnphr-:O2 0.292 -0.630 -0.353 -0.354 -0.707 0.489 0.500
## convergence code: 0
## boundary (singular) fit: see ?isSingularlattice::dotplot(ranef(dl5))## $subject
##
## $word
1.4.4 Post-hoc tests (multiple comparisons, Tukey-corrected):
dl5em <- emmeans(dl5, pairwise~position*sylpos)
dl5em.tab<-dl5em$contrasts %>%
summary(infer = T)
dl5em.tab$p.value<-lapply(dl5em.tab$p.value, FUN = function (x) {
# levels of p-values
if (x>=0.05) p <- "n.\\,s." # not significant
if (x<0.05) p <- "<0,05" # significant
if (x<=0.01) p <- "<0,01" # significant
if (x<=0.001) p <- "<0,001" # significant
return(p)
})
dl5em.tab<-as.data.frame(dl5em.tab)
colnames(dl5em.tab)<-c("Comparison","$\\beta$","s","df","Lower CI","Upper CI","t","p")
dl5em.tab.sig<-subset(dl5em.tab, p!="n.\\,s.")
dl5em.tab.sig## Comparison $\\beta$ s df
## 3 phrase-medial,N1 - phrase-final,N2 -101.19806 2.644951 6.811796
## 4 phrase-medial,N1 - phrase-medial,O1 -22.69792 2.280421 750.000456
## 5 phrase-medial,N1 - phrase-final,O1 -31.28447 2.559851 5.977351
## 6 phrase-medial,N1 - phrase-medial,O2 -35.46492 2.280421 750.000456
## 7 phrase-medial,N1 - phrase-final,O2 -55.90139 2.559851 5.977351
## 9 phrase-final,N1 - phrase-final,N2 -92.87703 2.377012 750.368370
## 10 phrase-final,N1 - phrase-medial,O1 -14.37688 2.559851 5.977351
## 11 phrase-final,N1 - phrase-final,O1 -22.96344 2.280421 750.000456
## 12 phrase-final,N1 - phrase-medial,O2 -27.14389 2.559851 5.977351
## 13 phrase-final,N1 - phrase-final,O2 -47.58036 2.280421 750.000456
## 14 phrase-medial,N2 - phrase-final,N2 -94.88477 2.649853 6.863926
## 15 phrase-medial,N2 - phrase-medial,O1 -16.38463 2.286408 750.016868
## 16 phrase-medial,N2 - phrase-final,O1 -24.97118 2.564669 6.024249
## 17 phrase-medial,N2 - phrase-medial,O2 -29.15164 2.286408 750.016868
## 18 phrase-medial,N2 - phrase-final,O2 -49.58810 2.564669 6.024249
## 19 phrase-final,N2 - phrase-medial,O1 78.50015 2.644951 6.811796
## 20 phrase-final,N2 - phrase-final,O1 69.91359 2.377012 750.368370
## 21 phrase-final,N2 - phrase-medial,O2 65.73314 2.644951 6.811796
## 22 phrase-final,N2 - phrase-final,O2 45.29667 2.377012 750.368370
## 24 phrase-medial,O1 - phrase-medial,O2 -12.76701 2.280421 750.000456
## 25 phrase-medial,O1 - phrase-final,O2 -33.20348 2.559851 5.977351
## 27 phrase-final,O1 - phrase-final,O2 -24.61692 2.280421 750.000456
## 28 phrase-medial,O2 - phrase-final,O2 -20.43647 2.559851 5.977351
## Lower CI Upper CI t p
## 3 -112.16716 -90.228969 -38.260840 <0,001
## 4 -29.62924 -15.766594 -9.953389 <0,001
## 5 -42.38136 -20.187581 -12.221210 <0,001
## 6 -42.39625 -28.533601 -15.551920 <0,001
## 7 -66.99828 -44.804503 -21.837756 <0,001
## 9 -100.10193 -85.652128 -39.073021 <0,001
## 10 -25.47377 -3.279992 -5.616297 <0,05
## 11 -29.89476 -16.032113 -10.069823 <0,001
## 12 -38.24078 -16.046999 -10.603700 <0,001
## 13 -54.51168 -40.649035 -20.864726 <0,001
## 14 -105.84781 -83.921738 -35.807557 <0,001
## 15 -23.33415 -9.435109 -7.166100 <0,001
## 16 -36.05765 -13.884715 -9.736609 <0,001
## 17 -36.10116 -22.202115 -12.749970 <0,001
## 18 -60.67457 -38.501636 -19.335086 <0,001
## 19 67.53105 89.469239 29.679240 <0,001
## 20 62.68869 77.138491 29.412389 <0,001
## 21 54.76405 76.702232 24.852305 <0,001
## 22 38.07177 52.521570 19.056141 <0,001
## 24 -19.69833 -5.835684 -5.598531 <0,001
## 25 -44.30036 -22.106586 -12.970865 <0,001
## 27 -31.54824 -17.685599 -10.794903 <0,001
## 28 -31.53336 -9.339580 -7.983462 <0,011.4.5 Disyllabic effect plot
Preparation
ef2b<-as.data.frame(effect("position*sylpos*tenseness",d5))
ef2b$sylpos<-factor(ef2b$sylpos, levels=c("O1","N1","O2","N2"))
ef2b$position<-factor(ef2b$position, levels=c("phrase-medial","phrase-final"))
ef2b$tenseness<-factor(ef2b$tenseness, levels=c("tense","lax"))
(ef2b.plot<-
ef2b %>% ggplot(aes(x=sylpos, y=fit,col=position,group=position,lty=position)) +
geom_point() +
geom_line()+
# geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
facet_grid(~tenseness)+
scale_color_brewer(palette = "Set1")+
xlab("Syllable position")+
ylab("")+
ylim(30,160)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
1.5 Figure 3: Mono- and disyllabic effect plot
ggpubr::ggarrange(ef1b.plot, ef2b.plot, common.legend = T,
labels = c("a)","b)"),font.label = list(size=11,face="plain"),align = "h",nrow=1)
ggsave(file="mono-di-acoustic-effects.pdf", width = 170, height = 100, units = "mm")1.6 Table 3 and 4
x<-capture.output(
texreg::texreg(list(texreg::extract(m5, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
# texreg::extract(d5, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
# texreg::extract(mf3.1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
),
digits = 1,booktabs = T,custom.model.names = c("Duration in monosyllabic words"),
float.pos = "!htbp", label = "tab:acousticresults.mono", use.packages = F,single.row = T,caption.above = T,
caption="Model predictions for the acoustic duration of syllable position in monosyllabic words
(standards error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final),
syllable constituent (\\emph{sylcon}, O = onset, N = nucleus, C = coda), and tenseness (tense vs. lax).",
dcolumn = T, fontsize = "scriptsize",center=F)
)
x[5]<-paste0(x[5], "\\centering")
x[27]<-paste0(x[27],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(m5.r2dummy)[1],2),"/",round(r.squaredGLMM(m5.r2dummy)[2],2),
# "&", round(r.squaredGLMM(mf3.1)[1],2), "/", round(r.squaredGLMM(mf3.1)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{Model predictions for the acoustic duration of syllable position in monosyllabic words
## (standards error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final),
## syllable constituent (\emph{sylcon}, O = onset, N = nucleus, C = coda), and tenseness (tense vs. lax).}\centering
## \begin{scriptsize}
## \begin{tabular}{l D{)}{)}{10)3} }
## \toprule
## & \multicolumn{1}{c}{Duration in monosyllabic words} \\
## \midrule
## (Intercept) & 93.8 \; (6.3)^{***} \\
## positionphrase-final & 10.6 \; (4.7)^{*} \\
## sylposN & 6.3 \; (3.3) \\
## sylposC & -29.4 \; (3.3)^{***} \\
## tensenesslax & -5.9 \; (8.1) \\
## positionphrase-final:sylposN & 34.1 \; (4.6)^{***} \\
## positionphrase-final:sylposC & 53.2 \; (4.6)^{***} \\
## positionphrase-final:tensenesslax & -2.7 \; (5.4) \\
## sylposN:tensenesslax & -31.5 \; (4.6)^{***} \\
## sylposC:tensenesslax & 13.2 \; (4.6)^{**} \\
## positionphrase-final:sylposN:tensenesslax & -16.1 \; (6.5)^{*} \\
## positionphrase-final:sylposC:tensenesslax & 11.9 \; (6.5) \\
## \midrule
## AIC & 22939.7 \\
## Num. obs. & 2342 \\
## Num. groups: subject & 10 \\
## Num. groups: word & 8 \\\midrule R$_m^2$/R$_c^2$ & 0.29/0.41\\
##
## \bottomrule
## \multicolumn{2}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{scriptsize}
## \label{tab:acousticresults.mono}
## \end{table} x<-capture.output(
texreg::texreg(list(texreg::extract(d5, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
# texreg::extract(d5, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
# texreg::extract(mf3.1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
),
digits = 1,booktabs = T,custom.model.names = c("Duration in disyllabic words"),
float.pos = "!htbp", label = "tab:acousticresults.di", use.packages = F,single.row = T,caption.above = T,
caption="Model predictions for the acoustic duration of syllable position in disyllabic words
(standards error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final),
syllable position (\\emph{sylcon}, O1/O2 = first/second onset, N1/N2 = first/second nucleus), and tenseness (tense vs. lax).",
dcolumn = T, fontsize = "scriptsize",center=F)
)
x[5]<-paste0(x[5], "\\centering")
x[31]<-paste0(x[31],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(d5.r2dummy)[1],2),"/", round(r.squaredGLMM(d5.r2dummy)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{Model predictions for the acoustic duration of syllable position in disyllabic words
## (standards error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final),
## syllable position (\emph{sylcon}, O1/O2 = first/second onset, N1/N2 = first/second nucleus), and tenseness (tense vs. lax).}\centering
## \begin{scriptsize}
## \begin{tabular}{l D{)}{)}{11)3} }
## \toprule
## & \multicolumn{1}{c}{Duration in disyllabic words} \\
## \midrule
## (Intercept) & 75.7 \; (14.4)^{***} \\
## positionphrase-final & 4.7 \; (2.9) \\
## sylposN1 & -1.7 \; (18.9) \\
## sylposO2 & 3.8 \; (12.5) \\
## sylposN2 & -31.9 \; (18.4) \\
## tensenesslax & -7.8 \; (19.9) \\
## positionphrase-final:sylposN1 & 14.5 \; (3.1)^{***} \\
## positionphrase-final:sylposO2 & 11.8 \; (3.1)^{***} \\
## positionphrase-final:sylposN2 & 100.0 \; (3.1)^{***} \\
## positionphrase-final:tensenesslax & 3.4 \; (4.1) \\
## sylposN1:tensenesslax & -21.3 \; (26.8) \\
## sylposO2:tensenesslax & 8.8 \; (17.7) \\
## sylposN2:tensenesslax & 15.3 \; (26.1) \\
## positionphrase-final:sylposN1:tensenesslax & -14.1 \; (4.4)^{**} \\
## positionphrase-final:sylposO2:tensenesslax & 0.4 \; (4.4) \\
## positionphrase-final:sylposN2:tensenesslax & -11.1 \; (4.5)^{*} \\
## \midrule
## AIC & 12857.9 \\
## Num. obs. & 1541 \\
## Num. groups: subject & 10 \\
## Num. groups: word & 4 \\\midrule R$_m^2$/R$_c^2$ & 0.68/0.81\\
##
## \bottomrule
## \multicolumn{2}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{scriptsize}
## \label{tab:acousticresults.di}
## \end{table}1.7 Table 9 and 10: Post-hoc tests (multiple comparisons, Tukey-corrected):
m5em <- emmeans(m5, pairwise~position*sylpos*tenseness)
m5em.tab<-m5em$contrasts %>%
summary(infer = T)
m5em.tab$p.value<-lapply(m5em.tab$p.value, FUN = function (x) {
# levels of p-values
if (x>=0.05) p <- "n.\\,s." # not significant
if (x<0.05) p <- "<0.05" # significant
if (x<=0.01) p <- "<0.01" # significant
if (x<=0.001) p <- "<0.001" # significant
return(p)
})
m5em.tab<-as.data.frame(m5em.tab)
colnames(m5em.tab)<-c("Comparison","$\\beta$","s","df","Lower CI","Upper CI","t","p")
m5em.tab.sig<-subset(m5em.tab, p!="n.\\,s.")
m5em.tab.sig<-m5em.tab.sig[-grep(pattern = ",O,",x = m5em.tab.sig$Comparison),]
rownames(m5em.tab.sig)<-seq(1,nrow(m5em.tab.sig),1)
print(xtable::xtable(m5em.tab.sig,label="tab:acoustic.mono.post",
caption = "Significant contrasts for monosyllabic target words with duration as dependent variable,
the predictors position (phrase-medial/-final), syllable position (O = Onset, N = Nucleus, C = Coda),
and tenseness (tense/lax), as well as estimated value $\\beta$, standard deviation (s), degrees of freedom (df), 95\\,\\% confidence
intervals, and t-values, Tukey-corrected for multiple comparisons.",
align = "llrrrrrrr",digits = c(0,0,1,1,1,2,2,2,2)),
include.colnames = T,include.rownames=T,booktabs = T, size = "\\scriptsize",
caption.placement = "top", sanitize.text.function = function(x) x)## % latex table generated in R 3.6.0 by xtable 1.8-4 package
## % Wed Jan 20 16:03:46 2021
## \begin{table}[ht]
## \centering
## \caption{Significant contrasts for monosyllabic target words with duration as dependent variable,
## the predictors position (phrase-medial/-final), syllable position (O = Onset, N = Nucleus, C = Coda),
## and tenseness (tense/lax), as well as estimated value $\beta$, standard deviation (s), degrees of freedom (df), 95\,\% confidence
## intervals, and t-values, Tukey-corrected for multiple comparisons.}
## \label{tab:acoustic.mono.post}
## \begingroup\scriptsize
## \begin{tabular}{llrrrrrrr}
## \toprule
## & Comparison & $\beta$ & s & df & Lower CI & Upper CI & t & p \\
## \midrule
## 1 & phrase-medial,N,tense - phrase-final,N,tense & -44.7 & 4.6 & 25.9 & -61.31 & -28.10 & -9.64 & <0.001 \\
## 2 & phrase-medial,N,tense - phrase-medial,C,tense & 35.7 & 3.3 & 2291.0 & 25.00 & 46.38 & 10.92 & <0.001 \\
## 3 & phrase-medial,N,tense - phrase-final,C,tense & -28.0 & 4.6 & 25.9 & -44.63 & -11.42 & -6.04 & <0.001 \\
## 4 & phrase-medial,N,tense - phrase-medial,N,lax & 37.4 & 8.1 & 7.6 & 1.54 & 73.24 & 4.63 & <0.05 \\
## 5 & phrase-medial,N,tense - phrase-final,C,lax & -44.5 & 9.7 & 8.1 & -86.77 & -2.14 & -4.57 & <0.05 \\
## 6 & phrase-final,N,tense - phrase-medial,C,tense & 80.4 & 4.6 & 25.9 & 63.79 & 97.00 & 17.34 & <0.001 \\
## 7 & phrase-final,N,tense - phrase-final,C,tense & 16.7 & 3.2 & 2291.0 & 6.15 & 27.21 & 5.18 & <0.001 \\
## 8 & phrase-final,N,tense - phrase-medial,N,lax & 82.1 & 10.0 & 8.9 & 39.74 & 124.45 & 8.23 & <0.001 \\
## 9 & phrase-final,N,tense - phrase-final,N,lax & 56.2 & 10.7 & 6.8 & 7.03 & 105.32 & 5.24 & <0.05 \\
## 10 & phrase-final,N,tense - phrase-medial,C,lax & 73.1 & 10.0 & 8.9 & 30.75 & 115.46 & 7.33 & <0.01 \\
## 11 & phrase-medial,C,tense - phrase-final,C,tense & -63.7 & 4.6 & 25.9 & -80.32 & -47.11 & -13.74 & <0.001 \\
## 12 & phrase-medial,C,tense - phrase-final,C,lax & -80.1 & 9.7 & 8.1 & -122.47 & -37.83 & -8.24 & <0.001 \\
## 13 & phrase-final,C,tense - phrase-medial,N,lax & 65.4 & 10.0 & 8.9 & 23.06 & 107.77 & 6.56 & <0.01 \\
## 14 & phrase-final,C,tense - phrase-medial,C,lax & 56.4 & 10.0 & 8.9 & 14.07 & 98.78 & 5.66 & <0.01 \\
## 15 & phrase-medial,N,lax - phrase-final,N,lax & -25.9 & 4.6 & 25.6 & -42.50 & -9.34 & -5.60 & <0.001 \\
## 16 & phrase-medial,N,lax - phrase-final,C,lax & -81.8 & 4.6 & 25.6 & -98.42 & -65.27 & -17.70 & <0.001 \\
## 17 & phrase-final,N,lax - phrase-medial,C,lax & 16.9 & 4.6 & 25.7 & 0.34 & 33.51 & 3.66 & <0.05 \\
## 18 & phrase-final,N,lax - phrase-final,C,lax & -55.9 & 3.2 & 2291.0 & -66.43 & -45.43 & -17.42 & <0.001 \\
## 19 & phrase-medial,C,lax - phrase-final,C,lax & -72.9 & 4.6 & 25.7 & -89.44 & -56.27 & -15.74 & <0.001 \\
## \bottomrule
## \end{tabular}
## \endgroup
## \end{table} print(xtable::xtable(d5em.tab.sig,label="tab:acoustic.di.post",
caption = "Significant contrasts for disyllabic target words with duration as dependent variable,
the predictors position (phrase-medial/-final), syllable position (O1/O2 = first/second onset, N1/N2 = first/second nucleus, C = coda),
and tenseness (tense/lax), as well as estimated value $\\beta$, standard deviation (s), degrees of freedom (df), 95\\,\\% confidence
intervals, and t-values, Tukey-corrected for multiple comparisons.",
align = "llrrrrrrr",digits = c(0,0,1,1,1,2,2,2,2)),
include.colnames = T,include.rownames=T,booktabs = T, size = "\\scriptsize",
caption.placement = "top", sanitize.text.function = function(x) x)## % latex table generated in R 3.6.0 by xtable 1.8-4 package
## % Wed Jan 20 16:03:46 2021
## \begin{table}[ht]
## \centering
## \caption{Significant contrasts for disyllabic target words with duration as dependent variable,
## the predictors position (phrase-medial/-final), syllable position (O1/O2 = first/second onset, N1/N2 = first/second nucleus, C = coda),
## and tenseness (tense/lax), as well as estimated value $\beta$, standard deviation (s), degrees of freedom (df), 95\,\% confidence
## intervals, and t-values, Tukey-corrected for multiple comparisons.}
## \label{tab:acoustic.di.post}
## \begingroup\scriptsize
## \begin{tabular}{llrrrrrrr}
## \toprule
## & Comparison & $\beta$ & s & df & Lower CI & Upper CI & t & p \\
## \midrule
## 1 & phrase-medial,N1,tense - phrase-final,N1,tense & -19.2 & 2.9 & 6.0 & -34.02 & -4.32 & -6.60 & <0.05 \\
## 2 & phrase-medial,N1,tense - phrase-medial,N2,tense & 30.3 & 2.4 & 6.2 & 18.36 & 42.17 & 12.88 & <0.001 \\
## 3 & phrase-medial,N1,tense - phrase-final,N2,tense & -74.4 & 2.7 & 2.9 & -95.43 & -53.38 & -27.51 & <0.01 \\
## 4 & phrase-final,N1,tense - phrase-medial,N2,tense & 49.4 & 3.3 & 2.5 & 20.01 & 78.86 & 14.82 & <0.05 \\
## 5 & phrase-final,N1,tense - phrase-final,N2,tense & -55.2 & 2.4 & 6.4 & -67.13 & -43.34 & -23.24 & <0.001 \\
## 6 & phrase-medial,O2,tense - phrase-final,O2,tense & -16.5 & 2.9 & 6.0 & -31.36 & -1.66 & -5.69 & <0.05 \\
## 7 & phrase-medial,O2,tense - phrase-final,O2,lax & -21.4 & 3.0 & 5.6 & -37.12 & -5.70 & -7.20 & <0.05 \\
## 8 & phrase-medial,O2,tense - phrase-final,N2,lax & -68.8 & 7.1 & 2.3 & -135.86 & -1.71 & -9.70 & <0.05 \\
## 9 & phrase-final,O2,tense - phrase-medial,N2,tense & 52.2 & 4.6 & 2.2 & 6.55 & 97.90 & 11.29 & <0.05 \\
## 10 & phrase-final,O2,tense - phrase-medial,N1,lax & 51.1 & 5.5 & 2.6 & 3.55 & 98.55 & 9.23 & <0.05 \\
## 11 & phrase-final,O2,tense - phrase-medial,O2,lax & 15.5 & 2.8 & 6.2 & 1.44 & 29.51 & 5.59 & <0.05 \\
## 12 & phrase-final,O2,tense - phrase-medial,N2,lax & 44.7 & 5.1 & 2.7 & 2.60 & 86.80 & 8.76 & <0.05 \\
## 13 & phrase-medial,N2,tense - phrase-final,N2,tense & -104.7 & 2.9 & 6.4 & -119.44 & -89.90 & -35.50 & <0.001 \\
## 14 & phrase-medial,N2,tense - phrase-final,O2,lax & -57.1 & 4.9 & 2.8 & -95.74 & -18.50 & -11.74 & <0.05 \\
## 15 & phrase-medial,N2,tense - phrase-final,N2,lax & -104.5 & 8.1 & 2.2 & -184.38 & -24.62 & -12.96 & <0.05 \\
## 16 & phrase-final,N2,tense - phrase-medial,N1,lax & 103.5 & 8.3 & 2.3 & 23.98 & 183.02 & 12.53 & <0.05 \\
## 17 & phrase-final,N2,tense - phrase-medial,O2,lax & 67.9 & 6.7 & 2.4 & 4.74 & 131.11 & 10.09 & <0.05 \\
## 18 & phrase-final,N2,tense - phrase-medial,N2,lax & 97.1 & 8.0 & 2.2 & 17.49 & 176.81 & 12.17 & <0.05 \\
## 19 & phrase-medial,N1,lax - phrase-final,O2,lax & -56.0 & 5.1 & 2.2 & -106.93 & -4.97 & -11.07 & <0.05 \\
## 20 & phrase-medial,N1,lax - phrase-final,N2,lax & -103.3 & 2.7 & 3.1 & -123.62 & -83.03 & -37.66 & <0.001 \\
## 21 & phrase-final,N1,lax - phrase-final,N2,lax & -94.8 & 2.4 & 7.0 & -106.60 & -83.09 & -39.02 & <0.001 \\
## 22 & phrase-medial,O2,lax - phrase-final,O2,lax & -20.4 & 2.9 & 6.0 & -35.24 & -5.52 & -7.02 & <0.05 \\
## 23 & phrase-final,O2,lax - phrase-medial,N2,lax & 49.6 & 4.6 & 2.2 & 3.91 & 95.29 & 10.72 & <0.05 \\
## 24 & phrase-medial,N2,lax - phrase-final,N2,lax & -97.0 & 3.0 & 6.7 & -111.64 & -82.31 & -32.51 & <0.001 \\
## \bottomrule
## \end{tabular}
## \endgroup
## \end{table}2 Articulatory analysis
# Articulatory analysis ####
# read mono- and disyllabic data
mono.art<-read.table("mono.art.txt", sep="\t", fileEncoding = "utf-8")
mono.art$tenseness<-factor(mono.art$tenseness,levels=c("tense","lax"))
mono.art$position<-factor(mono.art$position,levels=c("phrase-medial","phrase-final"))
di.art<-read.table("di.art.txt", sep="\t", fileEncoding = "utf-8")
di.art$tenseness<-factor(di.art$tenseness,levels=c("tense","lax"))
di.art$position<-factor(di.art$position,levels=c("phrase-medial","phrase-final"))
# data contains the following columns:
# 1: Source name
# 2: Trajectory name
# 3: Comment
# 4: Gesture onset at time point
# 5: Peak velocity of closing at time point
# 6: nucleus (plateau) onset at time point
# 7: Maximum constriction at time point
# 8: nucleus (plateau) offset at timepoint
# 9: Peak velocity of opening at time point
# 10: Gesture offset at time point
# 11: Gesture onset of anterior-posterior dimension at position
# 12: Peak velocity of closing of anterior-posterior dimension at position
# 13: nucleus (plateau) onset of anterior-posterior dimension at position
# 14: Maximum constriction of anterior-posterior dimension at position
# 15: nucleus (plateau) offset of anterior-posterior dimension at position
# 16: Peak velocity of closing of anterior-posterior dimension at position
# 17: Gesture offset of anterior-posterior dimension at position
# 18: Gesture onset of inferior-superior dimension at position
# 19: Peak velocity of closing of inferior-superior dimension at position
# 20: nucleus (plateau) onset of inferior-superior dimension at position
# 21: Maximum constriction of inferior-superior dimension at position
# 22: nucleus (plateau) offset of inferior-superior dimension at position
# 23: Peak velocity of closing of inferior-superior dimension at position
# 24: Gesture offset of inferior-superior dimension at position
# 25-31: velocities in cm/sec
# 32: subject id
# 33: sex
# 34: phrasal position
# 35: target word
# 36: repetition
# 37: plateau duration (platdur)
# 38: opening duration (opdur)
# 39: closing duration (closdur)
# 40: opening displacement (opdisp)
# 41: closing displacement (closdisp)2.1 Data removal and data used
Remove all items with closing or opening displacement below 1mm Monosyllabic 389 target words were originally included:
addmargins(table(mono.art$word,mono.art$position))##
## phrase-medial phrase-final Sum
## Bahn 50 49 99
## Bann 48 51 99
## Beet 47 51 98
## Bett 48 49 97
## Ruhm 49 47 96
## Rum 49 48 97
## Stiel 48 51 99
## still 50 49 99
## Sum 389 395 784Disyllabic 198 target words were originally included:
addmargins(table(di.art$word,di.art$position))##
## phrase-medial phrase-final Sum
## Huete 49 50 99
## Huette 51 48 99
## Miete 49 47 96
## Mitte 49 50 99
## Sum 198 195 393 ggplot(mono.art, aes(x=word, y=closdisp))+
geom_point()+
geom_hline(yintercept=1)
ggplot(mono.art, aes(x=word, y=opdisp))+
geom_point()+
geom_hline(yintercept=1)
ggplot(di.art, aes(x=word, y=opdisp))+
geom_point()+
geom_hline(yintercept=1)
mono.art.norm<- mono.art[mono.art$opdisp>1 & mono.art$closdisp>1,]
nrow(mono.art)-nrow(mono.art.norm)## [1] 36 #' 748 stimuli after data exclusion:
addmargins(table(mono.art.norm$word,mono.art.norm$position))##
## phrase-medial phrase-final Sum
## Bahn 50 49 99
## Bann 48 51 99
## Beet 47 51 98
## Bett 48 48 96
## Ruhm 39 33 72
## Rum 49 40 89
## Stiel 47 51 98
## still 49 48 97
## Sum 377 371 748 # nrow(di.art[di.art$opdisp<1 | di.art$closdisp<1 ,])
di.art.norm<- di.art[di.art$opdisp>1 & di.art$closdisp>1,]
nrow(di.art)-nrow(di.art.norm)## [1] 7 # invalid data of closing duration outlier, remove:
di.art.norm<-di.art.norm[di.art.norm$closdur>15,]
nrow(di.art)-nrow(di.art.norm)## [1] 8385 stimuli after data exclusion:
addmargins(table(di.art.norm$word,di.art.norm$position))##
## phrase-medial phrase-final Sum
## Huete 49 49 98
## Huette 49 45 94
## Miete 49 46 95
## Mitte 48 50 98
## Sum 195 190 385Total data excluded in the articulatory analysis
# nrow(mono.art)/nrow(mono.art.norm)-1Monosyllabic
sprintf("%1.2f%%",round((1- nrow(mono.art.norm)/800)*100),2)## [1] "6.00%"Disyllabic
#nrow(di.art)/nrow(di.art.norm)-1
sprintf("%1.2f%%",round((1- nrow(di.art.norm)/400)*100),2)## [1] "4.00%"** Total total **
sprintf("%1.2f%%",round((1- ((nrow(di.art.norm)+nrow(mono.art.norm))/(400+800)) )*100,2))## [1] "5.58%"Number of target words used in the articulatory analysis
addmargins(table(rbind(mono.art.norm,di.art.norm)$word, rbind(mono.art.norm,di.art.norm)$position))##
## phrase-medial phrase-final Sum
## Bahn 50 49 99
## Bann 48 51 99
## Beet 47 51 98
## Bett 48 48 96
## Ruhm 39 33 72
## Rum 49 40 89
## Stiel 47 51 98
## still 49 48 97
## Huete 49 49 98
## Huette 49 45 94
## Miete 49 46 95
## Mitte 48 50 98
## Sum 572 561 1133 mono.art$wordpair<-NULL
mono.art$wordpair[mono.art$word=="Bahn" | mono.art$word=="Bann"] <-"Bahn/Bann"
mono.art$wordpair[mono.art$word=="Beet" | mono.art$word=="Bett"] <-"Beet/Bett"
mono.art$wordpair[mono.art$word=="Ruhm" | mono.art$word=="Rum"] <-"Ruhm/Rum"
mono.art$wordpair[mono.art$word=="Stiel" | mono.art$word=="still"] <-"Stiel/still"
di.art$wordpair<-NULL
di.art$wordpair[di.art$word=="Huete" | di.art$word=="Huette"] <-"Huete/Huette"
di.art$wordpair[di.art$word=="Miete" | di.art$word=="Mitte"] <-"Miete/Mitte"2.2 Monosyllabic target words
# mono.art.norm$wordpair<-NULL
# mono.art.norm$wordpair[mono.art.norm$word=="Bahn" | mono.art.norm$word=="Bann"] <-"Bahn/Bann"
# mono.art.norm$wordpair[mono.art.norm$word=="Beet" | mono.art.norm$word=="Bett"] <-"Beet/Bett"
# mono.art.norm$wordpair[mono.art.norm$word=="Ruhm" | mono.art.norm$word=="Rum"] <-"Ruhm/Rum"
# mono.art.norm$wordpair[mono.art.norm$word=="Stiel" | mono.art.norm$word=="still"] <-"Stiel/still"2.2.1 Closing duration
# Closing duration mono ####
clodur1<-lmer(log(closdur) ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = mono.art)
qqPlot(resid(clodur1))## 11312 2532
## 674 740 abline(h = cut <- .6)
mono.art.sub1<-droplevels(subset(mono.art, resid(clodur1) < cut))
clodur2<-lmer(log(closdur) ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = mono.art.sub1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00228399 (tol = 0.002, component 1) qqPlot(resid(clodur2))
## 217 220
## 86 89 summary(clodur2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(closdur) ~ position + tenseness + (1 + position + tenseness |
## subject) + (1 | word)
## Data: mono.art.sub1
##
## REML criterion at convergence: 241.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7846 -0.5867 0.0197 0.6400 2.6637
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 0.008854 0.09410
## positionphrase-final 0.002446 0.04945 -0.20
## tensenesslax 0.008308 0.09115 -0.13 0.23
## word (Intercept) 0.029372 0.17138
## Residual 0.072934 0.27006
## Number of obs: 760, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.84979 0.09230 7.56129 41.710 3.25e-10 ***
## positionphrase-final 0.32404 0.02509 9.10505 12.915 3.67e-07 ***
## tensenesslax -0.06291 0.12610 6.66174 -0.499 0.634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.123
## tensenesslx -0.657 0.034
## convergence code: 0
## Model failed to converge with max|grad| = 0.00228399 (tol = 0.002, component 1) plot(allEffects(clodur2))
clodur3.int<-lmer(log(closdur) ~ position * tenseness + (1|subject) + (1|word), data = mono.art.sub1)
anova(clodur2, clodur3.int) # not better## refitting model(s) with ML (instead of REML)## Data: mono.art.sub1
## Models:
## clodur3.int: log(closdur) ~ position * tenseness + (1 | subject) + (1 | word)
## clodur2: log(closdur) ~ position + tenseness + (1 + position + tenseness |
## clodur2: subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## clodur3.int 7 254.00 286.44 -120.00 240.00
## clodur2 11 251.44 302.41 -114.72 229.44 10.565 4 0.03192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # failed
clodur2.ms<-lmer(closdur ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = mono.art.sub1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0351818 (tol = 0.002, component 1) summary(clodur2.ms)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdur ~ position + tenseness + (1 + position + tenseness |
## subject) + (1 | word)
## Data: mono.art.sub1
##
## REML criterion at convergence: 6399.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5627 -0.6386 -0.0711 0.4827 3.9194
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 22.28 4.720
## positionphrase-final 10.23 3.199 0.84
## tensenesslax 19.35 4.399 -0.15 0.23
## word (Intercept) 83.25 9.124
## Residual 250.50 15.827
## Number of obs: 760, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.875 4.903 7.571 10.173 1.11e-05 ***
## positionphrase-final 18.331 1.531 9.470 11.970 4.96e-07 ***
## tensenesslax -3.672 6.699 6.671 -0.548 0.601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn 0.081
## tensenesslx -0.663 0.032
## convergence code: 0
## Model failed to converge with max|grad| = 0.0351818 (tol = 0.002, component 1) plot(allEffects(clodur2.ms))
#subset lax
lax.cd.mono<-mono.art.sub1 %>% filter(tenseness == "lax") %>% droplevels()
cd.lax.mono.1<-lmer(log(closdur) ~ position + (1|subject) + (1|word), data = lax.cd.mono)
qqPlot(resid(cd.lax.mono.1))
## 12910 1211
## 258 200 plot(allEffects(cd.lax.mono.1))
summary(cd.lax.mono.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(closdur) ~ position + (1 | subject) + (1 | word)
## Data: lax.cd.mono
##
## REML criterion at convergence: 98.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2177 -0.6486 -0.0523 0.6784 2.7575
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.01569 0.1252
## word (Intercept) 0.02665 0.1633
## Residual 0.06801 0.2608
## Number of obs: 380, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.78426 0.09267 4.62551 40.84 4.27e-07 ***
## positionphrase-final 0.32868 0.02680 366.23567 12.27 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn -0.1432.2.2 Plateau duration
platdur1<-lmer(log(platdur) ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = mono.art)## boundary (singular) fit: see ?isSingular qqPlot(resid(platdur1))## 745 546
## 259 188 abline(h = cut <- c(-1.1,1.3))
mono.art.plat1<-droplevels(subset(mono.art, resid(platdur1) > cut[1] & resid(platdur1) < cut[2]))
platdur2<-lmer(log(platdur) ~ position + tenseness + (1|subject) + (1|word), data = mono.art.plat1)
qqPlot(resid(platdur2))
## 219 1018
## 88 331 summary(platdur2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(platdur) ~ position + tenseness + (1 | subject) + (1 | word)
## Data: mono.art.plat1
##
## REML criterion at convergence: 1040.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2799 -0.6406 0.0006 0.6190 3.0363
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.02695 0.1642
## word (Intercept) 0.10009 0.3164
## Residual 0.21825 0.4672
## Number of obs: 748, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.43041 0.16905 7.40951 20.292 9.17e-08 ***
## positionphrase-final 0.81460 0.03426 730.53104 23.779 < 2e-16 ***
## tensenesslax 0.20352 0.22630 6.00295 0.899 0.403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.096
## tensenesslx -0.669 -0.001 plot(allEffects(platdur2))
platdur3<-lmer(log(platdur) ~ position * tenseness + (1|subject) + (1|word), data = mono.art.plat1)
anova(platdur2,platdur3) #no## refitting model(s) with ML (instead of REML)## Data: mono.art.plat1
## Models:
## platdur2: log(platdur) ~ position + tenseness + (1 | subject) + (1 | word)
## platdur3: log(platdur) ~ position * tenseness + (1 | subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## platdur2 6 1044.3 1072.0 -516.14 1032.3
## platdur3 7 1045.6 1077.9 -515.81 1031.6 0.6731 1 0.412 platdur2.ms<-lmer(platdur ~ position + tenseness + (1|subject) + (1|word), data = mono.art.plat1)
summary(platdur2.ms)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: platdur ~ position + tenseness + (1 | subject) + (1 | word)
## Data: mono.art.plat1
##
## REML criterion at convergence: 7864.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9226 -0.6470 -0.1173 0.3996 5.7967
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 207.5 14.41
## word (Intercept) 747.3 27.34
## Residual 2081.8 45.63
## Number of obs: 748, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 29.878 14.690 7.480 2.034 0.0789 .
## positionphrase-final 62.360 3.346 730.642 18.640 <2e-16 ***
## tensenesslax 14.227 19.617 6.007 0.725 0.4956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.108
## tensenesslx -0.668 -0.0022.2.3 Closing displacement
closdis1<-lmer(closdisp ~ position + tenseness +(1+position+tenseness|subject) + (1|word), data = mono.art)## boundary (singular) fit: see ?isSingular qqPlot(resid(closdis1))## 1384 1792
## 467 690 abline(h = cut <- 10)
mono.art.disp1<-droplevels(subset(mono.art, resid(closdis1) < cut))
closdis2<-lmer(closdisp ~ position + tenseness +(1+position|subject) + (1|word), data = mono.art.disp1)
qqPlot(resid(closdis2))
## 1792 378
## 689 135 summary(closdis2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 + position | subject) +
## (1 | word)
## Data: mono.art.disp1
##
## REML criterion at convergence: 3526.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7968 -0.6069 -0.0275 0.5838 3.3944
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 2.0615 1.4358
## positionphrase-final 0.3725 0.6104 -0.72
## word (Intercept) 32.7455 5.7224
## Residual 4.7875 2.1880
## Number of obs: 783, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.64946 2.90017 6.30870 2.638 0.0369 *
## positionphrase-final 0.62736 0.24847 8.91719 2.525 0.0327 *
## tensenesslax -0.05315 4.04935 5.99701 -0.013 0.9900
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.104
## tensenesslx -0.698 0.000 plot(allEffects(closdis2))
# final is more displaced than medial2.2.4 Closing velocity
closvel1<-lmer(PVEL..V.cm.sec. ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = mono.art)
qqPlot(resid(closvel1))## 1024 1030
## 345 351 abline(h = cut <- 18)
mono.art.vel1<-droplevels(subset(mono.art, resid(closvel1) < cut))
closvel2<-lmer(PVEL..V.cm.sec. ~ position + tenseness + (1+position|subject) + (1|word), data = mono.art.vel1)
qqPlot(resid(closvel2))
## 1792 1384
## 688 465 summary(closvel2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 + position | subject) +
## (1 | word)
## Data: mono.art.vel1
##
## REML criterion at convergence: 4920.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3003 -0.6261 0.0040 0.5993 3.2141
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 27.744 5.267
## positionphrase-final 5.859 2.421 -0.61
## word (Intercept) 162.302 12.740
## Residual 28.321 5.322
## Number of obs: 782, groups: subject, 10; word, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 20.466 6.592 6.837 3.105 0.01774 *
## positionphrase-final -3.248 0.855 8.922 -3.799 0.00429 **
## tensenesslax 1.661 9.016 6.001 0.184 0.85995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.151
## tensenesslx -0.684 0.000 plot(allEffects(closvel2))
# dotplot(ranef(closvel2))
# final is sign. slower than medialIn none of the models with position and tenseness as fixed and subjects and words as random effects does tenseness affect the outcome variable ## Articulatory disyllabic analysis #### ### Closing duration
di.clodur1<-lmer(closdur ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = di.art)## boundary (singular) fit: see ?isSingular qqPlot(resid(di.clodur1))## 323 443
## 78 98 abline(h = cut <- 20)
di.art.sub1<-droplevels(subset(di.art, resid(di.clodur1) < cut))
di.clodur2<-lmer(closdur ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = di.art.sub1)
di.clodur3<-lmer(closdur ~ position + tenseness +
(1+position+tenseness|subject) + (1|word), data = di.art.sub1)
anova(di.clodur2,di.clodur3,refit=F)## Data: di.art.sub1
## Models:
## di.clodur2: closdur ~ position + tenseness + (1 + position + tenseness |
## di.clodur2: subject) + (1 | word)
## di.clodur3: closdur ~ position + tenseness + (1 + position + tenseness |
## di.clodur3: subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## di.clodur2 11 2754.3 2797.4 -1366.1 2732.3
## di.clodur3 11 2754.3 2797.4 -1366.1 2732.3 0 0 1 qqPlot(resid(di.clodur2))
## 625 999
## 114 217 summary(di.clodur2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdur ~ position + tenseness + (1 + position + tenseness |
## subject) + (1 | word)
## Data: di.art.sub1
##
## REML criterion at convergence: 2732.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6575 -0.5757 -0.0672 0.6350 2.5307
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 42.93 6.552
## positionphrase-final 32.31 5.684 0.51
## tensenesslax 53.15 7.290 -0.64 -0.89
## word (Intercept) 13.30 3.647
## Residual 78.35 8.852
## Number of obs: 373, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.447 3.403 4.856 13.944 4.2e-05 ***
## positionphrase-final 3.382 2.020 8.701 1.674 0.130
## tensenesslax 2.480 4.412 3.644 0.562 0.607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn 0.219
## tensenesslx -0.676 -0.417 plot(allEffects(di.clodur2))
di.clodur.int<-lmer(closdur ~ position * tenseness + (1+position+tenseness|subject) + (1|word), data = di.art.sub1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00514629 (tol = 0.002, component 1) anova(di.clodur2,di.clodur.int) # not better## refitting model(s) with ML (instead of REML)## Data: di.art.sub1
## Models:
## di.clodur2: closdur ~ position + tenseness + (1 + position + tenseness |
## di.clodur2: subject) + (1 | word)
## di.clodur.int: closdur ~ position * tenseness + (1 + position + tenseness |
## di.clodur.int: subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## di.clodur2 11 2765.3 2808.5 -1371.7 2743.3
## di.clodur.int 12 2767.3 2814.4 -1371.7 2743.3 1e-04 1 0.9915 summary(di.clodur.int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdur ~ position * tenseness + (1 + position + tenseness |
## subject) + (1 | word)
## Data: di.art.sub1
##
## REML criterion at convergence: 2729.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6529 -0.5757 -0.0678 0.6338 2.5283
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 42.88 6.548
## positionphrase-final 32.33 5.686 0.51
## tensenesslax 53.14 7.289 -0.64 -0.89
## word (Intercept) 13.37 3.656
## Residual 78.58 8.865
## Number of obs: 373, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 47.44274 3.43796 4.98555 13.800
## positionphrase-final 3.39180 2.23267 12.91091 1.519
## tensenesslax 2.48910 4.51153 3.91963 0.552
## positionphrase-final:tensenesslax -0.01866 1.84432 340.87161 -0.010
## Pr(>|t|)
## (Intercept) 3.66e-05 ***
## positionphrase-final 0.153
## tensenesslax 0.611
## positionphrase-final:tensenesslax 0.992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- tnsnss
## pstnphrs-fn 0.139
## tensenesslx -0.683 -0.284
## pstnphrs-f: 0.134 -0.425 -0.201
## convergence code: 0
## Model failed to converge with max|grad| = 0.00514629 (tol = 0.002, component 1) # longer closing duration in final position2.2.5 Plateau duration di
di.platdur1<-lmer(platdur ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = di.art)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00407375 (tol = 0.002, component 1) qqPlot(resid(di.platdur1))## 9612 1135
## 343 245 abline(h = cut <- 30)
di.art.plat1<-droplevels(subset(di.art, resid(di.platdur1) < cut))
di.platdur2<-lmer(platdur ~ position + tenseness + (1+position|subject) + (1|word), data = di.art.plat1)
qqPlot(resid(di.platdur2))
## 1129 835
## 235 178 summary(di.platdur2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: platdur ~ position + tenseness + (1 + position | subject) + (1 |
## word)
## Data: di.art.plat1
##
## REML criterion at convergence: 3029.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.68392 -0.66897 -0.01788 0.73976 2.77799
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 34.74 5.894
## positionphrase-final 71.10 8.432 0.95
## word (Intercept) 1.87 1.368
## Residual 155.79 12.482
## Number of obs: 381, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.799 2.371 8.888 14.676 1.56e-07 ***
## positionphrase-final 6.152 2.958 9.036 2.080 0.0672 .
## tensenesslax -2.521 1.873 1.992 -1.346 0.3111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn 0.556
## tensenesslx -0.397 0.004 di.platdur.int<-lmer(platdur ~ position * tenseness + (1+position|subject) + (1|word), data = di.art.plat1)
anova(di.platdur2,di.platdur.int) # not better## refitting model(s) with ML (instead of REML)## Data: di.art.plat1
## Models:
## di.platdur2: platdur ~ position + tenseness + (1 + position | subject) + (1 |
## di.platdur2: word)
## di.platdur.int: platdur ~ position * tenseness + (1 + position | subject) + (1 |
## di.platdur.int: word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## di.platdur2 8 3054.7 3086.2 -1519.3 3038.7
## di.platdur.int 9 3056.2 3091.7 -1519.1 3038.2 0.4336 1 0.5102 summary(di.platdur.int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: platdur ~ position * tenseness + (1 + position | subject) + (1 |
## word)
## Data: di.art.plat1
##
## REML criterion at convergence: 3024.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72167 -0.67304 0.01466 0.71742 2.80798
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 34.763 5.896
## positionphrase-final 71.253 8.441 0.95
## word (Intercept) 1.892 1.375
## Residual 156.008 12.490
## Number of obs: 381, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 35.218 2.456 10.158 14.341 4.52e-08
## positionphrase-final 5.299 3.226 12.711 1.643 0.125
## tensenesslax -3.351 2.254 4.114 -1.487 0.209
## positionphrase-final:tensenesslax 1.711 2.564 357.833 0.667 0.505
##
## (Intercept) ***
## positionphrase-final
## tensenesslax
## positionphrase-final:tensenesslax
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- tnsnss
## pstnphrs-fn 0.391
## tensenesslx -0.462 0.222
## pstnphrs-f: 0.256 -0.397 -0.552 # final has longer plateau phase than medial2.2.6 Closing displacement di
di.closdis1<-lmer(closdisp ~ position + tenseness + (1+position|subject) + (1|word), data = di.art)
qqPlot(resid(di.closdis1))
## 671 276
## 152 46 summary(di.closdis1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 + position | subject) +
## (1 | word)
## Data: di.art
##
## REML criterion at convergence: 1439.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0103 -0.7152 -0.0486 0.5136 3.9933
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 1.2143 1.1020
## positionphrase-final 0.4043 0.6358 -0.29
## word (Intercept) 1.0049 1.0024
## Residual 2.0090 1.4174
## Number of obs: 393, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.3029 0.7995 3.0638 6.632 0.00654 **
## positionphrase-final -0.4030 0.2468 8.9582 -1.633 0.13705
## tensenesslax 2.4667 1.0126 1.9996 2.436 0.13520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.154
## tensenesslx -0.633 0.000 plot(allEffects(di.closdis1))
di.closdis.int<-lmer(closdisp ~ position * tenseness +
(1+position+tenseness|subject) + (1|word), data = di.art)
anova(di.closdis1,di.closdis.int) # interaction for displacement better, but only slightly## refitting model(s) with ML (instead of REML)## Data: di.art
## Models:
## di.closdis1: closdisp ~ position + tenseness + (1 + position | subject) +
## di.closdis1: (1 | word)
## di.closdis.int: closdisp ~ position * tenseness + (1 + position + tenseness |
## di.closdis.int: subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## di.closdis1 8 1456.9 1488.7 -720.45 1440.9
## di.closdis.int 12 1419.5 1467.2 -697.74 1395.5 45.407 4 3.271e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# warrants closer inspection for differences in vowel height
# final is less (!) displaced than medial, other than for mono-words2.2.7 Closing velocity di
di.closvel1<-lmer(PVEL..V.cm.sec. ~ position + tenseness + (1+position+tenseness|subject) + (1|word), data = di.art)
qqPlot(resid(di.closvel1))
## 1355 301
## 313 61 summary(di.closvel1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 + position + tenseness |
## subject) + (1 | word)
## Data: di.art
##
## REML criterion at convergence: 2175.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2309 -0.5824 -0.0087 0.5525 2.8584
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 30.052 5.4820
## positionphrase-final 2.995 1.7306 -0.35
## tensenesslax 0.305 0.5523 0.03 0.18
## word (Intercept) 3.514 1.8744
## Residual 12.808 3.5788
## Number of obs: 393, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.1868 2.2046 8.6505 7.342 5.37e-05 ***
## positionphrase-final -2.2481 0.6558 8.9651 -3.428 0.00758 **
## tensenesslax 6.3497 1.9170 2.0321 3.312 0.07860 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.275
## tensenesslx -0.429 0.014 plot(allEffects(di.closvel1))
di.closvel.int<-lmer(PVEL..V.cm.sec. ~ position * tenseness + (1|subject) + (1|word), data = di.art)
summary(di.closvel.int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position * tenseness + (1 | subject) + (1 |
## word)
## Data: di.art
##
## REML criterion at convergence: 2175.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1417 -0.6210 -0.0223 0.5449 2.8800
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 27.673 5.261
## word (Intercept) 3.487 1.867
## Residual 13.309 3.648
## Number of obs: 393, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.6356 2.1556 8.5889 7.254 6.11e-05
## positionphrase-final -1.1541 0.5230 378.0212 -2.207 0.02794
## tensenesslax 7.4360 1.9380 2.1491 3.837 0.05499
## positionphrase-final:tensenesslax -2.1586 0.7371 378.0279 -2.929 0.00361
##
## (Intercept) ***
## positionphrase-final *
## tensenesslax .
## positionphrase-final:tensenesslax **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp- tnsnss
## pstnphrs-fn -0.121
## tensenesslx -0.450 0.134
## pstnphrs-f: 0.086 -0.710 -0.189 anova(di.closvel1,di.closvel.int) # not better## refitting model(s) with ML (instead of REML)## Data: di.art
## Models:
## di.closvel.int: PVEL..V.cm.sec. ~ position * tenseness + (1 | subject) + (1 |
## di.closvel.int: word)
## di.closvel1: PVEL..V.cm.sec. ~ position + tenseness + (1 + position + tenseness |
## di.closvel1: subject) + (1 | word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## di.closvel.int 7 2196.5 2224.3 -1091.2 2182.5
## di.closvel1 11 2204.4 2248.1 -1091.2 2182.4 0.1016 4 0.9988 plot(allEffects(di.closvel.int))
di.closvel.tenseness.med<-lmer(PVEL..V.cm.sec. ~ tenseness + (1|subject) + (1|word), data = di.art %>% filter(position=="phrase-medial"))
summary(di.closvel.tenseness.med)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ tenseness + (1 | subject) + (1 | word)
## Data: di.art %>% filter(position == "phrase-medial")
##
## REML criterion at convergence: 1098.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.64032 -0.60479 -0.03031 0.65028 2.84462
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 30.209 5.496
## word (Intercept) 3.511 1.874
## Residual 12.370 3.517
## Number of obs: 198, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.648 2.214 8.502 7.067 7.81e-05 ***
## tensenesslax 7.420 1.939 1.998 3.826 0.0621 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## tensenesslx -0.438 di.closvel.tenseness.fin<-lmer(PVEL..V.cm.sec. ~ tenseness + (1|subject) + (1|word), data = di.art %>% filter(position=="phrase-final"))
summary(di.closvel.tenseness.fin)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ tenseness + (1 | subject) + (1 | word)
## Data: di.art %>% filter(position == "phrase-final")
##
## REML criterion at convergence: 1090.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9937 -0.5744 -0.0512 0.5089 2.7097
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 26.613 5.159
## word (Intercept) 3.162 1.778
## Residual 12.981 3.603
## Number of obs: 195, groups: subject, 10; word, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.493 2.092 8.331 6.927 9.98e-05 ***
## tensenesslax 5.246 1.852 2.000 2.833 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## tensenesslx -0.443 di.closvel.position.tense<-lmer(PVEL..V.cm.sec. ~ position + (1|subject) + (1|word), data = di.art %>% filter(tenseness=="tense"))
summary(di.closvel.position.tense)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + (1 | subject) + (1 | word)
## Data: di.art %>% filter(tenseness == "tense")
##
## REML criterion at convergence: 1040.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72554 -0.55207 -0.08212 0.54756 2.42999
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 27.570 5.251
## word (Intercept) 6.414 2.533
## Residual 9.933 3.152
## Number of obs: 195, groups: subject, 10; word, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.6390 2.4628 3.2021 6.350 0.00648 **
## positionphrase-final -1.1492 0.4523 183.0282 -2.541 0.01188 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn -0.091 di.closvel.position.lax<-lmer(PVEL..V.cm.sec. ~ position + (1|subject) + (1|word), data = di.art %>% filter(tenseness=="lax"))
summary(di.closvel.position.lax)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + (1 | subject) + (1 | word)
## Data: di.art %>% filter(tenseness == "lax")
##
## REML criterion at convergence: 1148.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6941 -0.6793 0.0318 0.5785 2.6936
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 27.8615 5.278
## word (Intercept) 0.5344 0.731
## Residual 16.5143 4.064
## Number of obs: 198, groups: subject, 10; word, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 23.0729 1.7941 9.9770 12.861 1.55e-07 ***
## positionphrase-final -3.3084 0.5781 186.0504 -5.723 4.13e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pstnphrs-fn -0.160# final is sign. slower than medial2.3 Follow-up-models for mono-/disyllabic articulatory displacement and velocity
closdis.bahn<-lmer(closdisp ~ position + tenseness +(1+position|subject),
data = droplevels(subset(mono.art, wordpair == "Bahn/Bann")))
qqPlot(resid(closdis.bahn))
## 545 896
## 68 105plot(allEffects(closdis.bahn))
summary(closdis.bahn)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 + position | subject)
## Data: droplevels(subset(mono.art, wordpair == "Bahn/Bann"))
##
## REML criterion at convergence: 839.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.69602 -0.57113 -0.03963 0.54048 2.17252
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 16.27 4.034
## positionphrase-final 3.97 1.993 -0.54
## Residual 3.02 1.738
## Number of obs: 198, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.0963 1.2934 9.1508 12.445 4.84e-07 ***
## positionphrase-final 1.1871 0.6771 9.0038 1.753 0.113
## tensenesslax -1.6133 0.2475 177.1990 -6.520 7.10e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.534
## tensenesslx -0.093 -0.008# for Bahn/Bann no effect of position, but lax vowels are less displaced
closvel.bahn<-lmer(PVEL..V.cm.sec. ~ position + tenseness +(1+position|subject),
data = droplevels(subset(mono.art, wordpair == "Bahn/Bann")))
qqPlot(resid(closvel.bahn))
## 11100 2013
## 11 173plot(allEffects(closvel.bahn))
summary(closvel.bahn)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 + position | subject)
## Data: droplevels(subset(mono.art, wordpair == "Bahn/Bann"))
##
## REML criterion at convergence: 1276.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.08275 -0.58251 -0.03407 0.56592 2.70483
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 87.14 9.335
## positionphrase-final 19.46 4.411 -0.35
## Residual 29.66 5.446
## Number of obs: 198, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.1391 3.0267 9.2941 12.601 3.75e-07 ***
## positionphrase-final -4.7406 1.5966 8.9355 -2.969 0.0158 *
## tensenesslax 0.4271 0.7754 177.2831 0.551 0.5824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.361
## tensenesslx -0.125 -0.010# velocity is slower in final position, no effect for laxness
vh.beet<-droplevels(subset(mono.art, wordpair == "Beet/Bett"))
closdis.beet<-lmer(closdisp ~ position + tenseness +(1+position|subject),
data = vh.beet)
qqPlot(resid(closdis.beet))## 217 214
## 27 24abline(h = cut <- -4)
vh.beet1<-droplevels(subset(vh.beet, resid(closdis.beet) > cut))
closdis.beet1<-lmer(closdisp ~ position + tenseness +(1+position|subject),
data = vh.beet1)
qqPlot(resid(closdis.beet1))
## 220 3610
## 28 6plot(allEffects(closdis.beet1))
summary(closdis.beet1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 + position | subject)
## Data: vh.beet1
##
## REML criterion at convergence: 714.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14413 -0.54919 -0.01068 0.50285 2.79077
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 1.9743 1.4051
## positionphrase-final 0.5513 0.7425 -0.70
## Residual 2.0013 1.4147
## Number of obs: 193, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.8967 0.4792 9.9282 16.478 1.54e-08 ***
## positionphrase-final -0.2502 0.3111 8.8695 -0.804 0.442
## tensenesslax 0.8208 0.2039 172.4236 4.025 8.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.638
## tensenesslx -0.217 0.010# for Beet/Bett no effect of position, but lax vowels are more displaced
closvel.beet1<-lmer(PVEL..V.cm.sec. ~ position + tenseness +(1|subject),
data = vh.beet1)
qqPlot(resid(closvel.beet1))
## 1282 3610
## 141 6plot(allEffects(closvel.beet1))
summary(closvel.beet1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## Data: vh.beet1
##
## REML criterion at convergence: 1114.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2911 -0.5333 -0.0186 0.5657 3.0491
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 25.56 5.056
## Residual 16.39 4.048
## Number of obs: 193, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 22.0121 1.6792 10.2393 13.109 9.94e-08 ***
## positionphrase-final -3.9798 0.5836 181.0077 -6.819 1.32e-10 ***
## tensenesslax 5.5692 0.5833 181.0037 9.548 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.181
## tensenesslx -0.177 0.015closvel.beet2<-lmer(PVEL..V.cm.sec. ~ position * tenseness +(1|subject),
data = vh.beet1)
anova(closvel.beet1,closvel.beet2) # no sign. interaction## refitting model(s) with ML (instead of REML)## Data: vh.beet1
## Models:
## closvel.beet1: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## closvel.beet2: PVEL..V.cm.sec. ~ position * tenseness + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## closvel.beet1 5 1128.8 1145.1 -559.38 1118.8
## closvel.beet2 6 1130.0 1149.6 -559.02 1118.0 0.7139 1 0.3981# final position is slower; lax vowels lead to faster velocity (consistent with greater displacement)
vh.ruhm<-droplevels(subset(mono.art, wordpair == "Ruhm/Rum"))
closdis.ruhm<-lmer(closdisp ~ position + tenseness +(1|subject),
data = vh.ruhm)
qqPlot(resid(closdis.ruhm))
## 1671 1651
## 74 72plot(allEffects(closdis.ruhm))
summary(closdis.ruhm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 | subject)
## Data: vh.ruhm
##
## REML criterion at convergence: 565.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20549 -0.63934 -0.06795 0.54954 2.82855
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.1325 1.0642
## Residual 0.9215 0.9599
## Number of obs: 193, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6542 0.3570 10.5163 7.435 1.68e-05 ***
## positionphrase-final -0.1914 0.1385 181.0856 -1.382 0.169
## tensenesslax 1.6455 0.1383 181.0378 11.902 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.189
## tensenesslx -0.194 -0.006# for Ruhm/Rum no effect of position, but lax vowels are more displaced
closvel.ruhm<-lmer(PVEL..V.cm.sec. ~ position + tenseness +(1|subject),
data = vh.ruhm)
qqPlot(resid(closvel.ruhm))## 1671 1651
## 74 72abline(h = cut <- 6)
vh.ruhm.vel<-droplevels(subset(vh.ruhm, resid(closvel.ruhm) < cut))
closvel.ruhm1<-lmer(PVEL..V.cm.sec. ~ position + tenseness +(1|subject),
data = vh.ruhm.vel)
plot(allEffects(closvel.ruhm1))
summary(closvel.ruhm1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## Data: vh.ruhm.vel
##
## REML criterion at convergence: 840.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.79612 -0.70610 0.02063 0.56983 3.13554
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 5.983 2.446
## Residual 4.475 2.115
## Number of obs: 187, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.4081 0.8180 10.4252 7.834 1.10e-05 ***
## positionphrase-final -2.0843 0.3115 175.2438 -6.692 2.87e-10 ***
## tensenesslax 3.6482 0.3110 175.1901 11.732 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.188
## tensenesslx -0.181 -0.025closvel.ruhm1.int<-lmer(PVEL..V.cm.sec. ~ position * tenseness +(1|subject),
data = vh.ruhm.vel)
anova(closvel.ruhm1,closvel.ruhm1.int) # interaction not better## refitting model(s) with ML (instead of REML)## Data: vh.ruhm.vel
## Models:
## closvel.ruhm1: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## closvel.ruhm1.int: PVEL..V.cm.sec. ~ position * tenseness + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## closvel.ruhm1 5 850.36 866.52 -420.18 840.36
## closvel.ruhm1.int 6 849.23 868.62 -418.62 837.23 3.1301 1 0.07686 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vh.stiel<-droplevels(subset(mono.art, wordpair == "Stiel/still"))
closdis.stiel<-lmer(closdisp ~ position + tenseness +(1+position|subject),
data = vh.stiel)
qqPlot(resid(closdis.stiel))## 1384 1030
## 153 115abline(h = cut <- 5)
vh.stiel1<-droplevels(subset(vh.stiel, resid(closdis.stiel) < cut))
closdis.stiel1<-lmer(closdisp ~ position + tenseness +(1+position|subject),
data = vh.stiel1)
plot(allEffects(closdis.stiel1))
summary(closdis.stiel1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 + position | subject)
## Data: vh.stiel1
##
## REML criterion at convergence: 696.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6815 -0.5611 -0.0747 0.5507 2.3478
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 0.9549 0.9772
## positionphrase-final 0.3964 0.6296 -0.35
## Residual 1.7865 1.3366
## Number of obs: 195, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1797 0.3511 10.4941 11.903 2.0e-07 ***
## positionphrase-final 1.6091 0.2764 8.7510 5.822 0.000281 ***
## tensenesslax -1.3174 0.1918 174.3271 -6.868 1.1e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.415
## tensenesslx -0.273 0.010# for Stiel/still more displacement in final position and less displacement after lax vowels
closvel.stiel<-lmer(PVEL..V.cm.sec. ~ position + tenseness +(1+position|subject),
data = vh.stiel)
qqPlot(resid(closvel.stiel))## 1024 1384
## 109 153abline(h = cut <- 10)
vh.stiel.vel1<-droplevels(subset(vh.stiel, resid(closvel.stiel) < cut))
closvel.stiel1<-lmer(PVEL..V.cm.sec. ~ position + tenseness +(1|subject),
data = vh.stiel.vel1)
qqPlot(resid(closvel.stiel1))
## 612 11012
## 71 156plot(allEffects(closvel.stiel1))
summary(closvel.stiel1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## Data: vh.stiel.vel1
##
## REML criterion at convergence: 1081.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.60653 -0.61818 -0.07522 0.54715 2.80316
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 9.627 3.103
## Residual 15.249 3.905
## Number of obs: 191, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.321 1.099 12.063 13.027 1.82e-08 ***
## positionphrase-final -1.009 0.566 179.066 -1.783 0.0764 .
## tensenesslax -3.466 0.566 179.077 -6.123 5.65e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.265
## tensenesslx -0.263 0.0152.4 Table 6
# part 1
x<-capture.output(
texreg::texreg(list(texreg::extract(closdis.bahn, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closdis.beet1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closdis.ruhm, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closdis.stiel1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
),
digits = 1,booktabs = T,custom.model.names = c("Bahn/Bann","Beet/Bett","Ruhm/Rum","Stiel/still"),
float.pos = "!htbp", label = "tab:dispvel.mono", use.packages = F,single.row = T,caption.above = T,
caption="LME model predictions for the articulatory measurements of closing gesture displacement (ClosDisp) and closing gesture velocity (ClosVel) of the last consonant in all monosyllabic word pairs (standard error in parentheses) for phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax) in all monosyllabic target words.",
dcolumn = T, fontsize = "footnotesize",center=F)
)
x[3]<-paste0(x[3], "\\centering")
x[15]<-paste0(x[15],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(closdis.bahn)[1],2),"/",round(r.squaredGLMM(closdis.bahn)[2],2),
"&", round(r.squaredGLMM(closdis.beet1)[1],2),"/", round(r.squaredGLMM(closdis.beet1)[2],2),
"&", round(r.squaredGLMM(closdis.ruhm)[1],2), "/", round(r.squaredGLMM(closdis.ruhm)[2],2),
"&", round(r.squaredGLMM(closdis.stiel1)[1],2), "/", round(r.squaredGLMM(closdis.stiel1)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{LME model predictions for the articulatory measurements of closing gesture displacement (ClosDisp) and closing gesture velocity (ClosVel) of the last consonant in all monosyllabic word pairs (standard error in parentheses) for phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax) in all monosyllabic target words.}\centering
## \begin{footnotesize}
## \begin{tabular}{l D{)}{)}{9)3} D{)}{)}{9)3} D{)}{)}{9)3} D{)}{)}{9)3} }
## \toprule
## & \multicolumn{1}{c}{Bahn/Bann} & \multicolumn{1}{c}{Beet/Bett} & \multicolumn{1}{c}{Ruhm/Rum} & \multicolumn{1}{c}{Stiel/still} \\
## \midrule
## (Intercept) & 16.1 \; (1.3)^{***} & 7.9 \; (0.5)^{***} & 2.7 \; (0.4)^{***} & 4.2 \; (0.4)^{***} \\
## positionphrase-final & 1.2 \; (0.7) & -0.3 \; (0.3) & -0.2 \; (0.1) & 1.6 \; (0.3)^{***} \\
## tensenesslax & -1.6 \; (0.2)^{***} & 0.8 \; (0.2)^{***} & 1.6 \; (0.1)^{***} & -1.3 \; (0.2)^{***} \\
## \midrule
## AIC & 853.2 & 728.1 & 575.5 & 710.8 \\
## Num. obs. & 198 & 193 & 193 & 195 \\
## Num. groups: subject & 10 & 10 & 10 & 10 \\\midrule R$_m^2$/R$_c^2$ & 0.06/0.83&0.05/0.46&0.25/0.66&0.29/0.53\\
##
## \bottomrule
## \multicolumn{5}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{footnotesize}
## \label{tab:dispvel.mono}
## \end{table}# part 2
x<-capture.output(
texreg::texreg(list(texreg::extract(closvel.bahn, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closvel.beet1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closvel.ruhm1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closvel.stiel1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
),
digits = 1,booktabs = T,custom.model.names = c("Bahn/Bann","Beet/Bett","Ruhm/Rum","Stiel/still"),
float.pos = "!htbp", label = "tab:dispvel.mono", use.packages = F,single.row = T,caption.above = T,
caption="LME model predictions for the articulatory measurements of closing gesture displacement (ClosDisp) and closing gesture velocity (ClosVel) of the last consonant in all monosyllabic word pairs (standard error in parentheses) for phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax) in all monosyllabic target words.",
dcolumn = T, fontsize = "footnotesize",center=F)
)
x[3]<-paste0(x[3], "\\centering")
x[15]<-paste0(x[15],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(closvel.bahn)[1],2),"/",round(r.squaredGLMM(closvel.bahn)[2],2),
"&", round(r.squaredGLMM(closvel.beet1)[1],2),"/", round(r.squaredGLMM(closvel.beet1)[2],2),
"&", round(r.squaredGLMM(closvel.ruhm)[1],2), "/", round(r.squaredGLMM(closvel.ruhm)[2],2),
"&", round(r.squaredGLMM(closvel.stiel1)[1],2), "/", round(r.squaredGLMM(closvel.stiel1)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{LME model predictions for the articulatory measurements of closing gesture displacement (ClosDisp) and closing gesture velocity (ClosVel) of the last consonant in all monosyllabic word pairs (standard error in parentheses) for phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax) in all monosyllabic target words.}\centering
## \begin{footnotesize}
## \begin{tabular}{l D{)}{)}{9)3} D{)}{)}{9)3} D{)}{)}{9)3} D{)}{)}{9)3} }
## \toprule
## & \multicolumn{1}{c}{Bahn/Bann} & \multicolumn{1}{c}{Beet/Bett} & \multicolumn{1}{c}{Ruhm/Rum} & \multicolumn{1}{c}{Stiel/still} \\
## \midrule
## (Intercept) & 38.1 \; (3.0)^{***} & 22.0 \; (1.7)^{***} & 6.4 \; (0.8)^{***} & 14.3 \; (1.1)^{***} \\
## positionphrase-final & -4.7 \; (1.6)^{**} & -4.0 \; (0.6)^{***} & -2.1 \; (0.3)^{***} & -1.0 \; (0.6) \\
## tensenesslax & 0.4 \; (0.8) & 5.6 \; (0.6)^{***} & 3.6 \; (0.3)^{***} & -3.5 \; (0.6)^{***} \\
## \midrule
## AIC & 1290.1 & 1124.5 & 850.1 & 1091.8 \\
## Num. obs. & 198 & 193 & 187 & 191 \\
## Num. groups: subject & 10 & 10 & 10 & 10 \\\midrule R$_m^2$/R$_c^2$ & 0.05/0.75&0.22/0.7&0.28/0.64&0.12/0.46\\
##
## \bottomrule
## \multicolumn{5}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{footnotesize}
## \label{tab:dispvel.mono}
## \end{table}vh.di.miete<-droplevels(subset(di.art, wordpair=="Miete/Mitte"))
disp.miete<-lmer(closdisp ~ position + tenseness + (1|subject), data = vh.di.miete)
qqPlot(resid(disp.miete))## 671 276
## 73 26abline(h = cut <- 4)
vh.di.miete.sub<-subset(vh.di.miete, resid(disp.miete) < cut)
disp.miete1<-lmer(closdisp ~ position + tenseness + (1|subject), data = vh.di.miete.sub)
qqPlot(resid(disp.miete1))
## 1350 1163
## 146 121summary(disp.miete1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 | subject)
## Data: vh.di.miete.sub
##
## REML criterion at convergence: 664.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.04833 -0.55399 -0.09319 0.54075 2.71563
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.806 0.8978
## Residual 1.648 1.2837
## Number of obs: 192, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.3443 0.3257 12.7714 13.339 7.17e-09 ***
## positionphrase-final -0.3722 0.1855 180.1272 -2.006 0.0463 *
## tensenesslax 2.8328 0.1857 180.2153 15.251 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.275
## tensenesslx -0.279 -0.033# marginal effect for position; lax has greater displacement
vel.miete<-lmer(PVEL..V.cm.sec. ~ position + tenseness + (1|subject), data = vh.di.miete)
qqPlot(resid(vel.miete))
## 1355 290
## 154 35plot(allEffects(vel.miete))
summary(vel.miete)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## Data: vh.di.miete
##
## REML criterion at convergence: 1093
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5672 -0.6859 -0.0171 0.6101 2.0992
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 17.54 4.188
## Residual 13.95 3.735
## Number of obs: 195, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.3509 1.4031 10.4544 10.228 9.00e-07 ***
## positionphrase-final -2.2056 0.5350 183.0019 -4.122 5.68e-05 ***
## tensenesslax 7.5789 0.5353 183.0159 14.157 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.187
## tensenesslx -0.191 -0.016# slower velocity in final position; faster velocity after lax vowels
vh.di.huete<-droplevels(subset(di.art, wordpair=="Huete/Huette"))
disp.huete<-lmer(closdisp ~ position + tenseness + (1|subject), data = vh.di.huete)
qqPlot(resid(disp.huete))
## 323 625
## 40 63summary(disp.huete)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: closdisp ~ position + tenseness + (1 | subject)
## Data: vh.di.huete
##
## REML criterion at convergence: 697.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86832 -0.67487 0.05077 0.57674 2.93343
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.998 1.414
## Residual 1.690 1.300
## Number of obs: 198, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.1695 0.4750 10.5506 12.989 7.97e-08 ***
## positionphrase-final -0.3617 0.1848 186.0091 -1.957 0.0519 .
## tensenesslax 2.0572 0.1848 186.0108 11.130 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.196
## tensenesslx -0.198 0.020# no effect for position; lax has greater displacement
vel.huete<-lmer(PVEL..V.cm.sec. ~ position + tenseness + (1|subject), data = vh.di.huete)
qqPlot(resid(vel.huete))
## 301 9212
## 23 161plot(allEffects(vel.huete))
summary(vel.huete)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PVEL..V.cm.sec. ~ position + tenseness + (1 | subject)
## Data: vh.di.huete
##
## REML criterion at convergence: 1065.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8289 -0.5045 -0.0764 0.6045 3.3512
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 40.75 6.384
## Residual 10.58 3.253
## Number of obs: 198, groups: subject, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.0049 2.0584 9.4780 8.747 7.66e-06 ***
## positionphrase-final -2.2733 0.4626 186.0034 -4.914 1.95e-06 ***
## tensenesslax 5.1551 0.4626 186.0039 11.143 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pstnp-
## pstnphrs-fn -0.113
## tensenesslx -0.115 0.020# slower velocity in final position; faster velocity after lax vowels2.5 Fig 6: Articulatory effect plots
ef.m.closdur.pos<-as.data.frame(effect("position",clodur2.ms)) %>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.m.closdur.pos$position)<-c("medial","final")
ef.m.closdur.ten<-as.data.frame(effect("tenseness",clodur2))%>% mutate(tenseness= factor(tenseness, levels = c("tense","lax")))
ef.m.platdur.pos<-as.data.frame(effect("position",platdur2.ms)) %>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.m.platdur.pos$position)<-c("medial","final")
ef.m.platdur.ten<-as.data.frame(effect("tenseness",platdur2))%>% mutate(tenseness= factor(tenseness, levels = c("tense","lax")))
ef.m.closdis.pos<-as.data.frame(effect("position",closdis2)) %>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.m.closdis.pos$position)<-c("medial","final")
ef.m.closdis.ten<-as.data.frame(effect("tenseness",closdis2))%>% mutate(tenseness= factor(tenseness, levels = c("tense","lax")))
ef.m.closvel.pos<-as.data.frame(effect("position",closvel2)) %>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.m.closvel.pos$position)<-c("medial","final")
ef.m.closvel.ten<-as.data.frame(effect("tenseness",closvel2))%>% mutate(tenseness= factor(tenseness, levels = c("tense","lax")))
# disyllabic effect plot preparation
ef.d.closdur.pos<-as.data.frame(effect("position",di.clodur2))%>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.d.closdur.pos$position)<-c("medial","final")
ef.d.platdur.pos<-as.data.frame(effect("position",di.platdur2))%>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.d.platdur.pos$position)<-c("medial","final")
ef.d.closdis.pos<-as.data.frame(effect("position",di.closdis1))%>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.d.closdis.pos$position)<-c("medial","final")
ef.d.closvel.pos<-as.data.frame(effect("position",di.closvel1))%>% mutate(position= factor(position, levels = c("phrase-medial","phrase-final")))
levels(ef.d.closvel.pos$position)<-c("medial","final")
plevel <- function (x) {
if (x>0.05 ) p <- paste0("p = ",round(x,1)) # not significant
if (x<=0.05) p <- "p < 0.05" # significant
if (x<=0.01) p <- "p < 0.01" # significant
if (x<=0.001) p <- "p < 0.001" # significant
return(p)
}
(ef.m.closdur.pos.plot<-
ef.m.closdur.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=4.1,label=plevel(summary(clodur2)$coefficients[1,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Mono: ",plevel(summary(clodur2)$coefficients[2,5])))+
ylab("Closing duration (ms)")+
ylim(40,80)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.closdur.ten.plot<-
ef.m.closdur.ten %>%
ggplot(aes(x=tenseness, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
scale_color_brewer(palette = "Set1")+
xlab("Closing duration (log)")+
ylab("")+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.platdur.pos.plot<-
ef.m.platdur.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=4.1,label=plevel(summary(platdur2)$coefficients[1,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Mono: ",plevel(summary(platdur2)$coefficients[2,5])))+
ylab("Plateau duration (ms)")+
ylim(20,120)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.platdur.ten.plot<-
ef.m.platdur.ten %>%
ggplot(aes(x=tenseness, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
scale_color_brewer(palette = "Set1")+
xlab("")+
ylab("Plateau duration (log)")+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.closdis.pos.plot<-
ef.m.closdis.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=9,label=plevel(summary(closdis2)$coefficients[2,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Mono: ",plevel(summary(closdis2)$coefficients[2,5])))+
ylab("Closing displacement (mm)")+
ylim(5,11)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.closdis.ten.plot<-
ef.m.closdis.ten %>%
ggplot(aes(x=tenseness, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
scale_color_brewer(palette = "Set1")+
xlab("")+
ylab("Closing displacement (mm)")+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.closvel.pos.plot<-
ef.m.closvel.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=22,label=plevel(summary(closvel2)$coefficients[2,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Mono: ",plevel(summary(closvel2)$coefficients[2,5])))+
ylab("Closing velocity (cm/s)")+
ylim(12,28)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.m.closvel.ten.plot<-
ef.m.closvel.ten %>%
ggplot(aes(x=tenseness, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
scale_color_brewer(palette = "Set1")+
xlab("")+
ylab("Closing velocity (cm/s)")+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
# disyllabic effect plots
(ef.d.closdur.pos.plot<-
ef.d.closdur.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=55,label=plevel(summary(di.clodur2)$coefficients[2,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Di: ",plevel(summary(di.clodur2)$coefficients[2,5])))+
ylab("Closing duration (ms)")+
ylim(c(40,80))+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.d.platdur.pos.plot<-
ef.d.platdur.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=43,label=plevel(summary(di.platdur2)$coefficients[2,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Di: ",plevel(summary(di.platdur2)$coefficients[2,5])))+
ylab("Plateau duration (ms)")+
ylim(c(20,120))+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.d.closdis.pos.plot<-
ef.d.closdis.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=7.1,label=plevel(summary(di.closdis1)$coefficients[2,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Di: ",plevel(summary(di.closdis1)$coefficients[2,5])))+
ylab("Closing displacement (mm)")+
ylim(5,11)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
(ef.d.closvel.pos.plot<-
ef.d.closvel.pos %>%
ggplot(aes(x=position, y=fit)) +
geom_point() +
geom_line(aes(group=1),col="red")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=0.4,alpha=.8,size=0.8) +
# annotate("text",x=1.5,y=22,label=plevel(summary(di.closvel1)$coefficients[2,5]))+
scale_color_brewer(palette = "Set1")+
xlab(paste0("Di: ",plevel(summary(di.closvel1)$coefficients[2,5])))+
ylab("Closing velocity (cm/s)")+
ylim(12,28)+
theme_bw(base_size=10)+
theme(legend.position = "top",legend.title = element_blank()))
ggpubr::ggarrange(ef.m.closdur.pos.plot, ef.m.platdur.pos.plot, ef.m.closdis.pos.plot, ef.m.closvel.pos.plot,
# ef.m.closdur.ten.plot, ef.m.platdur.ten.plot, ef.m.closdis.ten.plot, ef.m.closvel.ten.plot,
ef.d.closdur.pos.plot, ef.d.platdur.pos.plot, ef.d.closdis.pos.plot, ef.d.closvel.pos.plot,
common.legend = T,
labels = c("a)","b)","c)","d)","e)","f)","g)","h)"),
font.label = list(size=11,face="plain"),align = "h",nrow=2,ncol=4)
ggsave(file="mono-art-effects.pdf", width = 200, height = 130, units = "mm")2.6 Table 5
x<-capture.output(
texreg::texreg(list(texreg::extract(clodur2, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(platdur2, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closdis2, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(closvel2, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
)
,
override.pvalues = list(summary(clodur2)$coefficients[1:3,5],
summary(platdur2)$coefficients[1:3,5],
summary(closdis2)$coefficients[1:3,5],
summary(closvel2)$coefficients[1:3,5])
,
digits = 1,booktabs = T,custom.model.names = c("ClosDur (log)","PlatDur (log)","ClosDisp","ClosVel"),
float.pos = "!htbp", label = "tab:articresults.mono", use.packages = F,single.row = T,caption.above = T,
caption="LME model predictions for the articulatory measurements of closing gesture duration (ClosDur), plateau duration (PlatDur), closing gesture displacement (ClosDisp), and closing gesture velocity (ClosVel) of the last consonant in all monosyllabic word pairs (standard error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax).",
dcolumn = T, fontsize = "footnotesize",center=F)
)## Warning in override(models, override.coef, override.se, override.pvalues, :
## P-values were provided using 'override.pval', but standard errors were not
## replaced! x[3]<-paste0(x[3], "\\centering")
x[16]<-paste0(x[16],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(clodur2)[1],2),"/",round(r.squaredGLMM(clodur2)[2],2),
"&", round(r.squaredGLMM(platdur2)[1],2),"/", round(r.squaredGLMM(platdur2)[2],2),
"&", round(r.squaredGLMM(closdis2)[1],2), "/", round(r.squaredGLMM(closdis2)[2],2),
"&", round(r.squaredGLMM(closvel2)[1],2), "/", round(r.squaredGLMM(closvel2)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{LME model predictions for the articulatory measurements of closing gesture duration (ClosDur), plateau duration (PlatDur), closing gesture displacement (ClosDisp), and closing gesture velocity (ClosVel) of the last consonant in all monosyllabic word pairs (standard error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax).}\centering
## \begin{footnotesize}
## \begin{tabular}{l D{)}{)}{9)3} D{)}{)}{8)3} D{)}{)}{9)1} D{)}{)}{9)2} }
## \toprule
## & \multicolumn{1}{c}{ClosDur (log)} & \multicolumn{1}{c}{PlatDur (log)} & \multicolumn{1}{c}{ClosDisp} & \multicolumn{1}{c}{ClosVel} \\
## \midrule
## (Intercept) & 3.8 \; (0.1)^{***} & 3.4 \; (0.2)^{***} & 7.6 \; (2.9)^{*} & 20.5 \; (6.6)^{*} \\
## positionphrase-final & 0.3 \; (0.0)^{***} & 0.8 \; (0.0)^{***} & 0.6 \; (0.2)^{*} & -3.2 \; (0.9)^{**} \\
## tensenesslax & -0.1 \; (0.1) & 0.2 \; (0.2) & -0.1 \; (4.0) & 1.7 \; (9.0) \\
## \midrule
## AIC & 263.1 & 1052.9 & 3542.9 & 4936.5 \\
## Num. obs. & 760 & 748 & 783 & 782 \\
## Num. groups: subject & 10 & 10 & 10 & 10 \\
## Num. groups: word & 8 & 8 & 8 & 8 \\\midrule R$_m^2$/R$_c^2$ & 0.19/0.49&0.34/0.58&0/0.88&0.02/0.87\\
##
## \bottomrule
## \multicolumn{5}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{footnotesize}
## \label{tab:articresults.mono}
## \end{table}2.7 Table 7
x<-capture.output(
texreg::texreg(list(texreg::extract(di.clodur2, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(di.platdur2, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(di.closdis1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F),
texreg::extract(di.closvel1, include.variance=F,include.nobs=T,include.bic=F,include.loglik=F)
),
override.pvalues = list(summary(di.clodur2)$coefficients[1:3,5],
summary(di.platdur2)$coefficients[1:3,5],
summary(di.closdis1)$coefficients[1:3,5],
summary(di.closvel1)$coefficients[1:3,5]),
digits = 1,booktabs = T,custom.model.names = c("ClosDur","PlatDur","ClosDisp","ClosVel"),
float.pos = "!htbp", label = "tab:articresults.di", use.packages = F,single.row = T,caption.above = T,
caption="LME model predictions for the articulatory measurements of closing gesture duration (ClosDur), plateau duration (PlatDur), closing gesture displacement (ClosDisp), and closing gesture velocity (ClosVel) of the last consonant in all disyllabic word pairs (standard error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax).",
dcolumn = T, fontsize = "footnotesize",center=F)
)## Warning in override(models, override.coef, override.se, override.pvalues, :
## P-values were provided using 'override.pval', but standard errors were not
## replaced! x[3]<-paste0(x[3], "\\centering")
x[16]<-paste0(x[16],"\\midrule R$_m^2$/R$_c^2$ & ", round(r.squaredGLMM(di.clodur2)[1],2),"/",round(r.squaredGLMM(di.clodur2)[2],2),
"&", round(r.squaredGLMM(di.platdur2)[1],2),"/", round(r.squaredGLMM(di.platdur2)[2],2),
"&", round(r.squaredGLMM(di.closdis1)[1],2), "/", round(r.squaredGLMM(di.closdis1)[2],2),
"&", round(r.squaredGLMM(di.closvel1)[1],2), "/", round(r.squaredGLMM(di.closvel1)[2],2),
"\\\\ \n")
cat(x, sep = "\n")##
## \begin{table}[!htbp]
## \caption{LME model predictions for the articulatory measurements of closing gesture duration (ClosDur), plateau duration (PlatDur), closing gesture displacement (ClosDisp), and closing gesture velocity (ClosVel) of the last consonant in all disyllabic word pairs (standard error in parentheses) for the fixed effects phrasal position (phrase-medial vs. phrase-final) and tenseness (tense vs. lax).}\centering
## \begin{footnotesize}
## \begin{tabular}{l D{)}{)}{9)3} D{)}{)}{9)3} D{)}{)}{9)2} D{)}{)}{9)3} }
## \toprule
## & \multicolumn{1}{c}{ClosDur} & \multicolumn{1}{c}{PlatDur} & \multicolumn{1}{c}{ClosDisp} & \multicolumn{1}{c}{ClosVel} \\
## \midrule
## (Intercept) & 47.4 \; (3.4)^{***} & 34.8 \; (2.4)^{***} & 5.3 \; (0.8)^{**} & 16.2 \; (2.2)^{***} \\
## positionphrase-final & 3.4 \; (2.0) & 6.2 \; (3.0) & -0.4 \; (0.2) & -2.2 \; (0.7)^{**} \\
## tensenesslax & 2.5 \; (4.4) & -2.5 \; (1.9) & 2.5 \; (1.0) & 6.3 \; (1.9) \\
## \midrule
## AIC & 2754.3 & 3045.1 & 1455.8 & 2197.5 \\
## Num. obs. & 373 & 381 & 393 & 393 \\
## Num. groups: subject & 10 & 10 & 10 & 10 \\
## Num. groups: word & 4 & 4 & 4 & 4 \\\midrule R$_m^2$/R$_c^2$ & 0.03/0.48&0.04/0.45&0.27/0.65&0.2/0.77\\
##
## \bottomrule
## \multicolumn{5}{l}{\tiny{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
## \end{tabular}
## \end{footnotesize}
## \label{tab:articresults.di}
## \end{table} # plot articulatory data in boxplots from data subsets used for the models above
# mono.art.sub1 # clodur mono
# mono.art.plat1 # platdur mono
# mono.art # clodis mono
# mono.art.vel1 # clovel mono
# di.art.sub1 # clodur di
# di.art.plat1 # platdur di
# di.art # clodis di
# di.art # clovel di
#' ## Figure 4
#### position on x-axis, tenseness as fill
m.clos<-ggplot(mono.art.sub1, aes(x=position, y=closdur,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Closing duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
m.plat<-ggplot(mono.art.plat1, aes(x=position, y=platdur,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Plateau duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
m.disp<-ggplot(mono.art, aes(x=position, y=closdisp,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Closing displacement (mm)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
m.vel<-ggplot(mono.art.vel1, aes(x=position, y=PVEL..V.cm.sec.,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Closing velocity (cm/s)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
#### tenseness on x-axis, position as fill
m.clos<-ggplot(mono.art.sub1, aes(x=tenseness, y=closdur,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Closing duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
m.plat<-ggplot(mono.art.plat1, aes(x=tenseness, y=platdur,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Plateau duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
m.disp<-ggplot(mono.art, aes(x=tenseness, y=closdisp,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Closing displacement (mm)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
m.vel<-ggplot(mono.art.vel1, aes(x=tenseness, y=PVEL..V.cm.sec.,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Closing velocity (cm/s)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
ggpubr::ggarrange(m.clos,m.plat,m.disp,m.vel,
common.legend = T,
labels = c("a)","b)","c)","d)"),
font.label = list(size=11,face="plain"),align = "h",nrow=2,ncol=2)
ggsave(file="mono-art-raw-tenseness-on-x.pdf", width = 250, height = 250, units = "mm")
#' ## Figure 5
# di.art.sub1 # clodur di
# di.art.plat1 # platdur di
# di.art # clodis di
# di.art # clovel di
d.clos<-ggplot(di.art.sub1, aes(x=position, y=closdur,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Closing duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
d.plat<-ggplot(di.art.plat1, aes(x=position, y=platdur,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Plateau duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
d.disp<-ggplot(di.art, aes(x=position, y=closdisp,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Closing displacement (mm)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
d.vel<-ggplot(di.art, aes(x=position, y=PVEL..V.cm.sec.,fill=tenseness))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
scale_x_discrete(labels=c("medial","final"))+
ylab("Closing velocity (cm/s)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
#### tenseness on x-axis, position as fill
d.clos<-ggplot(di.art.sub1, aes(x=tenseness, y=closdur,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Closing duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
d.plat<-ggplot(di.art.plat1, aes(x=tenseness, y=platdur,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Plateau duration (ms)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
d.disp<-ggplot(di.art, aes(x=tenseness, y=closdisp,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Closing displacement (mm)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
d.vel<-ggplot(di.art, aes(x=tenseness, y=PVEL..V.cm.sec.,fill=position))+
geom_point(position=position_jitterdodge(dodge.width = .75,jitter.width = .3,jitter.height = .2),alpha=.3,size=.7,col="black")+
geom_boxplot(alpha=.3,outlier.shape = NA)+
facet_grid(~wordpair)+
scale_fill_brewer(name="",palette="Set1")+
# scale_x_discrete(labels=c("medial","final"))+
ylab("Closing velocity (cm/s)")+
xlab("")+
theme_bw()+
theme(legend.position="top")
ggpubr::ggarrange(d.clos,d.plat,d.disp,d.vel,
common.legend = T,
labels = c("a)","b)","c)","d)"),
font.label = list(size=11,face="plain"),align = "h",nrow=2,ncol=2)
ggsave(file="di-art-raw-tenseness-on-x.pdf", width = 200, height = 200, units = "mm")3 Figure of proportional lengthening
sd.mono<-mono %>%
group_by(tenseness, position, word, sylpos) %>%
summarise(sylpos.sd = sd(sylposdur)) %>%
spread(position, sylpos.sd) %>%
rename( "medial.sd" = `phrase-medial`, "final.sd" = `phrase-final`)## `summarise()` regrouping output by 'tenseness', 'position', 'word' (override with `.groups` argument) a<-mono %>%
group_by(tenseness, position, word, sylpos) %>%
summarise(sylpos.mean = mean(sylposdur)) %>%
spread(position, sylpos.mean) %>%
rename( "medial" = `phrase-medial`, "final" = `phrase-final`) %>%
mutate(prop.len = final/medial,prop.len = prop.len-1,
wordpairs = recode_factor(word, "Bahn" = "Bahn/Bann", "Bann" = "Bahn/Bann",
"Beet"="Beet/Bett","Bett"="Beet/Bett",
"Ruhm"="Ruhm/Rum", "Rum" = "Ruhm/Rum",
"Stiel" = "Stiel/still", "still" = "Stiel/still"),
sylpos = recode_factor(sylpos, "C" = "Coda", "N" = "Nucleus", "O" = "Onset"),
sylpos = factor(sylpos, levels=c("Onset","Nucleus","Coda"))) %>%
ggplot(aes(x=word,y=prop.len, shape=tenseness,col=sylpos))+
geom_point()+
geom_line(aes(group=interaction(wordpairs, sylpos)))+
scale_y_continuous(labels = scales::percent,breaks = seq(0,2,.2), name = "Lengthening proportion")+
xlab("Target word")+
theme_bw()+
theme(legend.position = "top",legend.title = element_blank(),legend.box = "vertical")## `summarise()` regrouping output by 'tenseness', 'position', 'word' (override with `.groups` argument) b<-di %>%
group_by(tenseness, position, word, sylpos) %>%
summarise(sylpos.mean = mean(sylposdur)) %>%
spread(position, sylpos.mean) %>%
rename( "medial" = `phrase-medial`, "final" = `phrase-final`) %>%
mutate(prop.len = final/medial,prop.len = prop.len-1,
wordpairs = recode_factor(word, "Miete" = "Miete/Mitte", "Mitte" = "Miete/Mitte",
"Huete"="Hüte/Hütte","Huette"="Hüte/Hütte"),
sylpos = recode_factor(sylpos, "O1" = "1st onset", "N1" = "1st nucleus", "O2" = "2nd onset", "N2" = "2nd nucleus"),
sylpos = factor(sylpos, levels=c("1st onset","1st nucleus","2nd onset","2nd nucleus"))) %>%
ggplot(aes(x=word,y=prop.len, shape=tenseness,col=sylpos))+
geom_point()+
geom_line(aes(group=interaction(wordpairs, sylpos)))+
scale_y_continuous(labels = scales::percent,breaks = seq(0,2.6,.2), name = "")+
xlab("Target word")+
theme_bw()+
theme(legend.position = "top",legend.title = element_blank(),legend.box = "vertical",legend.text.align = 1)+
guides(shape = guide_legend(order = 2),col = guide_legend(order = 1))## `summarise()` regrouping output by 'tenseness', 'position', 'word' (override with `.groups` argument) cowplot::plot_grid(a,b, labels = c("a) Monosyllabic","b) Disyllabic"),label_fontface = "plain", label_size = 11,
rel_widths = c(1,1), nrow = 1)