GAMMs LabPhon 18
Jalal Al-Tamimi
Newcastle University6 June 2018
1 Loading packages
library(ggplot2);library(mgcv);library(itsadug);library(tidyverse);library(gganimate)2 Demo Linear Model vs GAM
In this demo, data obtained from a Jordanian Arabic speaker producing the word /du:d/ “worm” is used. F2 frequencies obtained at 5 ms interval from onset to midpoint of the vowel are used. Let’s plot the F2 frequencies by interval number. The second plot displays a linear regression line superimposed on the plot. As can be seen, the linear regression line is not able to cope with the curviness of the data, and is unable to cope with it. The third plot superimposes a spline obtained from a GAM model.
demoGAMProd <- read.csv("dataProd_duud.csv")
head(demoGAMProd)ggplot.demo1 <- ggplot(demoGAMProd,aes(x=IntervalsN,y=F2))+
geom_point()+theme_bw(base_size = 20)+coord_cartesian(ylim=c(600,1800))+
labs(y = "F2 Hz", x = "Intervals number")
ggplot.demo1
ggplot.demo2 <- ggplot(demoGAMProd,aes(x=IntervalsN,y=F2))+
geom_point()+theme_bw(base_size = 20)+coord_cartesian(ylim=c(600,1800))+
labs(y = "F2 Hz", x = "Intervals number")+geom_smooth(method = lm, se=F)
ggplot.demo2ggplot.demo3 <- ggplot(demoGAMProd,aes(x=IntervalsN,y=predict(bam(F2 ~ s(IntervalsN, bs="cr"), data = demoGAMProd))))+
geom_point()+theme_bw(base_size = 20)+coord_cartesian(ylim=c(600,1800))+
labs(y = "F2 Hz", x = "Intervals number")+geom_smooth(method = lm, formula = y ~ splines::bs(x, 3),se=F)
ggplot.demo3
3 Actual data analyses
This data is obtained from a single speaker producing various consonants in the frame VCV, with V = symmetric /i: a: u:/ (=vowel) and C is one of 17 consonants put into five contexts “plain”, “velar”, “uvular”, “pharyngealised” and “pharyngeal” (=context). The data is from Ultrasound Tongue Imaging. Tongue contours were obtained at the consonant onset, midpoint and offset (=time). A total of 51 words (=word) were obtained and these were repeated 3 times. Tongue splines were extracted with X-Y coordinates (=y outcome). 42 Fan positions (=Fan) were extracted and these are used as a normalisation between contexts. The outcome is y, the predictors are Fan, time, context and vowel, and the random factor is word.
3.1 Creating new variables
Before running the actual GAM model on the data, we need to:
- Create two new variables for context and vowel. These will be “ordered” predictors and sum coded
- To run an Auto-Regressive Model, we need to find the autocorrelation in the dataframe:
- We rearrange the data by time, ID, and Fan
- We add a new variable “start” that marks the start of Fan
pharDF <- read.csv("resultsFull.c_NoRelNoz.csv")
str(pharDF)'data.frame': 18900 obs. of 23 variables:
$ X.1 : int 1 2 3 4 5 6 7 8 9 10 ...
$ ID : int 1 2 3 7 8 9 10 11 12 13 ...
$ Client.Surname : chr "Sara" "Sara" "Sara" "Sara" ...
$ Time.of.sample.in.recording: num 3.84 5.37 2.37 5.3 2.26 ...
$ Prompt : chr "2aataa.... 2aataa.... 2aataa.... 2aattaa.... 2aattaa.... 2aattaa" "2aataa.... 2aataa.... 2aataa.... 2aattaa.... 2aattaa.... 2aattaa" "2aataa.... 2aataa.... 2aataa.... 2aattaa.... 2aattaa.... 2aattaa" "2aataa.... 2aataa.... 2aataa.... 2aattaa.... 2aattaa.... 2aattaa" ...
$ word : chr "2aataa" "2aataa" "2aataa" "2aataa" ...
$ context : chr "plain" "plain" "plain" "plain" ...
$ consonant : chr "t" "t" "t" "t" ...
$ consType : chr "plosive" "plosive" "plosive" "plosive" ...
$ vowel : chr "aa" "aa" "aa" "aa" ...
$ labelNew : chr "CL_Mid" "CL_Mid" "CL_Mid" "CL_Ons" ...
$ label : chr "CL_Mid" "CL_Mid" "CL_Mid" "CL_Ons" ...
$ repetition : int 2 3 1 3 1 2 3 1 2 2 ...
$ Fan : int 1 1 1 1 1 1 1 1 1 1 ...
$ x : num 18.8 18.9 21.5 19.1 17.7 ...
$ y : num 15.6 15.6 14.1 15.5 16.3 ...
$ vowel2 : chr "a<U+02D0>" "a<U+02D0>" "a<U+02D0>" "a<U+02D0>" ...
$ labelNew2 : int 2 2 2 1 1 1 3 3 3 2 ...
$ context.ord : chr "plain" "plain" "plain" "plain" ...
$ vowel.ord : chr "aa" "aa" "aa" "aa" ...
$ fan_line : int 1 1 1 1 1 1 1 1 1 1 ...
$ X : num 18.8 18.9 21.5 19.1 17.7 ...
$ Y : num 15.6 15.6 14.1 15.5 16.3 ...levels(pharDF$context)NULLpharDF$context <- factor(pharDF$context,
levels = c("plain","velar","uvular","pharyngealised","pharyngeal"))
levels(pharDF$vowel)NULLpharDF$vowel <- factor(pharDF$vowel,
levels = c("ii","aa","uu"))
levels(pharDF$labelNew)NULLpharDF$labelNew <- factor(pharDF$labelNew,
levels = c("CL_Ons","CL_Mid","CL_Off"))
pharDF$ID <- as.numeric(pharDF$ID)
pharDF$Fan <- as.numeric(pharDF$Fan)
pharDF$time <- pharDF$labelNew2
pharDF$context.ord <- as.ordered(pharDF$context)
contrasts(pharDF$context.ord) <- "contr.treatment"
pharDF$vowel.ord <- as.ordered(pharDF$vowel)
contrasts(pharDF$vowel.ord) <- "contr.treatment"
## to find autocorrelation in the data. Rearrange dataframe and add a variable "start" to indicate
## when Fan == 1
pharDF <- arrange(pharDF, time, ID, Fan)
pharDF$start <- pharDF$Fan==1
3.2 Visualising the data
Before running anything, we start by visualising the data and use an animated figure
pl <- ggplot(pharDF, aes(x=Fan, y=y, col=context, frame=time)) +
facet_grid(vowel ~ context) +
geom_line(aes(group=ID))+theme_bw()
gganimate(pl, ani.width=800, height=300)
3.3 Running our first GAM model
This is our first GAM model. This will be used to find the level of autocorrelation in the data. Our model has double non-linear predictors (fan and time), two factor predictors (context and vowel) and one random effect (word). Interactions between fan and time are used. In addition, by context*vowel interaction is added to account for the two-way interaction with respect to each of fan and time. The function itsadug::acf_resid is used, and we display the level of autocorrelation for each of the “Fan”, the “time”, the “context”, the “vowel” finally the “word”
phar.gam.noAR <- bam(y ~ context.ord*vowel.ord +
## 1d smooths
s(Fan, bs="cr", k=10) +
s(time, bs="cr", k=3) +
## 1d smooths * factors
s(Fan, k=10, bs="cr", by=context.ord) +
s(Fan, k=10, bs="cr", by=vowel.ord) +
s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
s(time, k=3, bs="cr", by=context.ord) +
s(time, k=3, bs="cr", by=vowel.ord) +
s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
## 2d smooths
ti(Fan, time, bs="cr", k=c(10,3))+
## 2d smooths * factors
ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
## random smooths by word
s(Fan, word, bs="fs", k=10, m=1) +
s(time, word, bs="fs", k=3, m=1),
data=pharDF)summary(phar.gam.noAR)
Family: gaussian
Link function: identity
Formula:
y ~ context.ord * vowel.ord + s(Fan, bs = "cr", k = 10) +
s(time, bs = "cr", k = 3) + s(Fan, k = 10, bs = "cr",
by = context.ord) + s(Fan, k = 10, bs = "cr", by = vowel.ord) +
s(Fan, k = 10, bs = "cr", by = context.ord:vowel.ord) +
s(time, k = 3, bs = "cr", by = context.ord) + s(time,
k = 3, bs = "cr", by = vowel.ord) + s(time, k = 3,
bs = "cr", by = context.ord:vowel.ord) + ti(Fan, time,
bs = "cr", k = c(10, 3)) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = vowel.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord:vowel.ord) + s(Fan, word,
bs = "fs", k = 10, m = 1) + s(time, word, bs = "fs",
k = 3, m = 1)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.8171 0.6348 56.422 <2e-16 ***
context.ordvelar 1.9427 0.8882 2.187 0.0287 *
context.orduvular 1.8558 1.2923 1.436 0.1510
context.ordpharyngealised 0.7096 0.8366 0.848 0.3964
context.ordpharyngeal 0.7807 1.0983 0.711 0.4772
vowel.ordaa 0.7598 0.8454 0.899 0.3688
vowel.orduu 0.8967 0.8357 1.073 0.2833
context.ordvelar:vowel.ordaa -0.9334 1.2093 -0.772 0.4402
context.orduvular:vowel.ordaa -2.2185 1.6007 -1.386 0.1658
context.ordpharyngealised:vowel.ordaa -1.3093 1.1544 -1.134 0.2567
context.ordpharyngeal:vowel.ordaa 0.2187 1.4674 0.149 0.8815
context.ordvelar:vowel.orduu -2.3741 1.2207 -1.945 0.0518 .
context.orduvular:vowel.orduu -2.6085 1.8942 -1.377 0.1685
context.ordpharyngealised:vowel.orduu -1.8392 1.0893 -1.688 0.0914 .
context.ordpharyngeal:vowel.orduu -1.0306 1.4959 -0.689 0.4909
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F
s(Fan) 8.657e+00 8.701e+00 97.019
s(time) 1.000e+00 1.000e+00 7.939
s(Fan):context.ordvelar 7.648e+00 7.828e+00 7.293
s(Fan):context.orduvular 7.716e+00 7.924e+00 9.315
s(Fan):context.ordpharyngealised 8.577e+00 8.654e+00 28.667
s(Fan):context.ordpharyngeal 1.000e+00 1.000e+00 3.486
s(Fan):vowel.ordaa 7.919e+00 8.070e+00 11.075
s(Fan):vowel.orduu 8.090e+00 8.236e+00 14.557
s(Fan):context.ord:vowel.ordplain:ii 5.927e+00 6.192e+00 7.076
s(Fan):context.ord:vowel.ordplain:aa 4.337e+00 4.580e+00 0.992
s(Fan):context.ord:vowel.ordplain:uu 1.000e+00 1.000e+00 0.051
s(Fan):context.ord:vowel.ordvelar:ii 6.347e-04 6.835e-04 0.200
s(Fan):context.ord:vowel.ordvelar:aa 2.253e+00 2.425e+00 1.104
s(Fan):context.ord:vowel.ordvelar:uu 7.454e+00 7.688e+00 7.822
s(Fan):context.ord:vowel.orduvular:ii 1.000e+00 1.000e+00 0.347
s(Fan):context.ord:vowel.orduvular:aa 1.366e-04 1.518e-04 0.297
s(Fan):context.ord:vowel.orduvular:uu 5.426e+00 5.727e+00 2.178
s(Fan):context.ord:vowel.ordpharyngealised:ii 1.000e+00 1.000e+00 2.134
s(Fan):context.ord:vowel.ordpharyngealised:aa 6.412e+00 6.703e+00 3.514
s(Fan):context.ord:vowel.ordpharyngealised:uu 1.283e-04 1.406e-04 0.008
s(Fan):context.ord:vowel.ordpharyngeal:ii 5.662e+00 5.956e+00 4.594
s(Fan):context.ord:vowel.ordpharyngeal:aa 1.000e+00 1.000e+00 0.381
s(Fan):context.ord:vowel.ordpharyngeal:uu 3.101e+00 3.348e+00 2.631
s(time):context.ordvelar 1.837e+00 1.848e+00 7.206
s(time):context.orduvular 1.000e+00 1.000e+00 2.293
s(time):context.ordpharyngealised 1.000e+00 1.000e+00 0.016
s(time):context.ordpharyngeal 1.000e+00 1.000e+00 1.495
s(time):vowel.ordaa 1.000e+00 1.000e+00 0.658
s(time):vowel.orduu 1.000e+00 1.000e+00 0.356
s(time):context.ord:vowel.ordplain:ii 7.865e-01 8.011e-01 4.599
s(time):context.ord:vowel.ordplain:aa 1.000e+00 1.000e+00 4.160
s(time):context.ord:vowel.ordplain:uu 1.482e+00 1.503e+00 0.580
s(time):context.ord:vowel.ordvelar:ii 1.173e-04 1.261e-04 0.128
s(time):context.ord:vowel.ordvelar:aa 8.014e-01 8.146e-01 4.954
s(time):context.ord:vowel.ordvelar:uu 1.000e+00 1.000e+00 0.392
s(time):context.ord:vowel.orduvular:ii 1.670e+00 1.689e+00 0.705
s(time):context.ord:vowel.orduvular:aa 3.499e-04 3.803e-04 0.666
s(time):context.ord:vowel.orduvular:uu 1.000e+00 1.000e+00 0.504
s(time):context.ord:vowel.ordpharyngealised:ii 1.000e+00 1.000e+00 0.237
s(time):context.ord:vowel.ordpharyngealised:aa 1.539e+00 1.561e+00 0.570
s(time):context.ord:vowel.ordpharyngealised:uu 6.326e-01 6.528e-01 2.638
s(time):context.ord:vowel.ordpharyngeal:ii 8.496e-01 8.608e-01 6.565
s(time):context.ord:vowel.ordpharyngeal:aa 1.000e+00 1.000e+00 0.481
s(time):context.ord:vowel.ordpharyngeal:uu 2.528e-04 2.748e-04 0.545
ti(Fan,time) 9.379e+00 1.097e+01 3.994
ti(Fan,time):context.ordvelar 1.017e+01 1.241e+01 3.547
ti(Fan,time):context.orduvular 1.466e+01 1.653e+01 13.907
ti(Fan,time):context.ordpharyngealised 9.661e+00 1.181e+01 1.892
ti(Fan,time):context.ordpharyngeal 1.841e+00 2.019e+00 13.035
ti(Fan,time):vowel.ordaa 1.377e+01 1.541e+01 10.850
ti(Fan,time):vowel.orduu 1.345e+01 1.535e+01 6.194
ti(Fan,time):context.ord:vowel.ordplain:ii 6.187e+00 7.119e+00 7.741
ti(Fan,time):context.ord:vowel.ordplain:aa 1.012e+01 1.220e+01 6.141
ti(Fan,time):context.ord:vowel.ordplain:uu 9.295e+00 1.126e+01 3.991
ti(Fan,time):context.ord:vowel.ordvelar:ii 9.355e-01 9.724e-01 14.881
ti(Fan,time):context.ord:vowel.ordvelar:aa 1.362e+01 1.583e+01 3.757
ti(Fan,time):context.ord:vowel.ordvelar:uu 3.429e+00 4.311e+00 2.001
ti(Fan,time):context.ord:vowel.orduvular:ii 1.881e+00 1.970e+00 4.217
ti(Fan,time):context.ord:vowel.orduvular:aa 1.000e+00 1.000e+00 0.686
ti(Fan,time):context.ord:vowel.orduvular:uu 7.832e+00 1.004e+01 3.334
ti(Fan,time):context.ord:vowel.ordpharyngealised:ii 1.527e+01 1.681e+01 30.704
ti(Fan,time):context.ord:vowel.ordpharyngealised:aa 8.195e-01 1.069e+00 0.037
ti(Fan,time):context.ord:vowel.ordpharyngealised:uu 1.000e+00 1.000e+00 0.260
ti(Fan,time):context.ord:vowel.ordpharyngeal:ii 1.133e+01 1.350e+01 27.316
ti(Fan,time):context.ord:vowel.ordpharyngeal:aa 7.579e+00 1.000e+01 2.397
ti(Fan,time):context.ord:vowel.ordpharyngeal:uu 1.344e+01 1.555e+01 15.310
s(Fan,word) 3.422e+02 4.850e+02 30.764
s(time,word) 9.068e+01 1.200e+02 11.467
p-value
s(Fan) < 2e-16 ***
s(time) 0.004843 **
s(Fan):context.ordvelar 1.76e-09 ***
s(Fan):context.orduvular 1.09e-12 ***
s(Fan):context.ordpharyngealised < 2e-16 ***
s(Fan):context.ordpharyngeal 0.061874 .
s(Fan):vowel.ordaa 8.33e-16 ***
s(Fan):vowel.orduu < 2e-16 ***
s(Fan):context.ord:vowel.ordplain:ii 1.41e-07 ***
s(Fan):context.ord:vowel.ordplain:aa 0.371509
s(Fan):context.ord:vowel.ordplain:uu 0.821906
s(Fan):context.ord:vowel.ordvelar:ii 0.990667
s(Fan):context.ord:vowel.ordvelar:aa 0.283912
s(Fan):context.ord:vowel.ordvelar:uu 4.35e-10 ***
s(Fan):context.ord:vowel.orduvular:ii 0.555667
s(Fan):context.ord:vowel.orduvular:aa 0.994640
s(Fan):context.ord:vowel.orduvular:uu 0.041265 *
s(Fan):context.ord:vowel.ordpharyngealised:ii 0.144101
s(Fan):context.ord:vowel.ordpharyngealised:aa 0.001135 **
s(Fan):context.ord:vowel.ordpharyngealised:uu 0.999177
s(Fan):context.ord:vowel.ordpharyngeal:ii 9.88e-05 ***
s(Fan):context.ord:vowel.ordpharyngeal:aa 0.537254
s(Fan):context.ord:vowel.ordpharyngeal:uu 0.042896 *
s(time):context.ordvelar 0.010347 *
s(time):context.orduvular 0.129965
s(time):context.ordpharyngealised 0.899964
s(time):context.ordpharyngeal 0.221516
s(time):vowel.ordaa 0.417237
s(time):vowel.orduu 0.550551
s(time):context.ord:vowel.ordplain:ii 0.054935 .
s(time):context.ord:vowel.ordplain:aa 0.041395 *
s(time):context.ord:vowel.ordplain:uu 0.619221
s(time):context.ord:vowel.ordvelar:ii 0.996792
s(time):context.ord:vowel.ordvelar:aa 0.044560 *
s(time):context.ord:vowel.ordvelar:uu 0.531006
s(time):context.ord:vowel.orduvular:ii 0.356142
s(time):context.ord:vowel.orduvular:aa 0.987300
s(time):context.ord:vowel.orduvular:uu 0.477786
s(time):context.ord:vowel.ordpharyngealised:ii 0.626791
s(time):context.ord:vowel.ordpharyngealised:aa 0.358487
s(time):context.ord:vowel.ordpharyngealised:uu 0.189473
s(time):context.ord:vowel.ordpharyngeal:ii 0.017459 *
s(time):context.ord:vowel.ordpharyngeal:aa 0.488045
s(time):context.ord:vowel.ordpharyngeal:uu 0.990237
ti(Fan,time) 6.97e-06 ***
ti(Fan,time):context.ordvelar 3.65e-05 ***
ti(Fan,time):context.orduvular < 2e-16 ***
ti(Fan,time):context.ordpharyngealised 0.057168 .
ti(Fan,time):context.ordpharyngeal 0.000187 ***
ti(Fan,time):vowel.ordaa < 2e-16 ***
ti(Fan,time):vowel.orduu 2.32e-13 ***
ti(Fan,time):context.ord:vowel.ordplain:ii 1.65e-09 ***
ti(Fan,time):context.ord:vowel.ordplain:aa 5.37e-11 ***
ti(Fan,time):context.ord:vowel.ordplain:uu 6.25e-06 ***
ti(Fan,time):context.ord:vowel.ordvelar:ii 0.000143 ***
ti(Fan,time):context.ord:vowel.ordvelar:aa 7.15e-07 ***
ti(Fan,time):context.ord:vowel.ordvelar:uu 0.066650 .
ti(Fan,time):context.ord:vowel.orduvular:ii 0.020633 *
ti(Fan,time):context.ord:vowel.orduvular:aa 0.407391
ti(Fan,time):context.ord:vowel.orduvular:uu 0.000240 ***
ti(Fan,time):context.ord:vowel.ordpharyngealised:ii < 2e-16 ***
ti(Fan,time):context.ord:vowel.ordpharyngealised:aa 0.829029
ti(Fan,time):context.ord:vowel.ordpharyngealised:uu 0.610342
ti(Fan,time):context.ord:vowel.ordpharyngeal:ii < 2e-16 ***
ti(Fan,time):context.ord:vowel.ordpharyngeal:aa 0.007720 **
ti(Fan,time):context.ord:vowel.ordpharyngeal:uu < 2e-16 ***
s(Fan,word) < 2e-16 ***
s(time,word) < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Rank: 1282/1303
R-sq.(adj) = 0.986 Deviance explained = 98.6%
fREML = 35503 Scale est. = 2.207 n = 18900rho_est <- start_value_rho(phar.gam.noAR)
rho_est[1] 0.94171073.3.1 ACF first GAM
The five plots below show the autocorrelation in the residuals. As can be see, the first plot using Fan, has a complete zero correlations. It is possible that because the various fan points obtained alongside the tongue contour are already correlated with each other, and hence the correlations between fan 1 to 2, 2 to 3, 41 to 42 are already taken into account by the model. The four additional plots show the autocorrelation in the residuals for time, context, vowel and word. All of these predictors are showing autocorrelations between their successive data points. This means that for a specific time (onset, midpoint or offset), there is already correlations between tongue splines obtained at the onset, the midpoint and the offset. It is crucial to tell the model to account for this, otherwise there is overconfidence in the estimates.
3.3.1.1 ACF by Fan
acf_resid(phar.gam.noAR,split_pred=list(pharDF$Fan),main = "Average ACF No.AR by Fan",cex.lab=1.5,cex.axis=1.5)
3.3.1.2 ACF by Time
acf_resid(phar.gam.noAR,split_pred=list(pharDF$time),main = "Average ACF No.AR by Time",cex.lab=1.5,cex.axis=1.5)
3.3.1.3 ACF by Context
acf_resid(phar.gam.noAR,split_pred=list(pharDF$context.ord),main = "Average ACF No.AR by Context",cex.lab=1.5,cex.axis=1.5)
3.3.1.4 ACF by Vowel
acf_resid(phar.gam.noAR,split_pred=list(pharDF$vowel.ord),main = "Average ACF No.AR by Vowel",cex.lab=1.5,cex.axis=1.5)
3.3.1.5 ACF by Word
acf_resid(phar.gam.noAR,split_pred=list(pharDF$word),main = "Average ACF No.AR by Word",cex.lab=1.5,cex.axis=1.5)
3.4 Running our second Autoregressive GAM
Our second GAM model is identical to the first, but takes into account the autocorrelation. By including AR.start and rho, we are using information obtained from original model to account for the autocorrelation
phar.gam.AR <- bam(y ~ context.ord*vowel.ord +
## 1d smooths
s(Fan, bs="cr", k=10) +
s(time, bs="cr", k=3) +
## 1d smooths * factors
s(Fan, k=10, bs="cr", by=context.ord) +
s(Fan, k=10, bs="cr", by=vowel.ord) +
s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
s(time, k=3, bs="cr", by=context.ord) +
s(time, k=3, bs="cr", by=vowel.ord) +
s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
## 2d smooths
ti(Fan, time, bs="cr", k=c(10,3))+
## 2d smooths * factors
ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
## random smooths by word
s(Fan, word, bs="fs", k=10, m=1) +
s(time, word, bs="fs", k=3, m=1),
data=pharDF,
AR.start=pharDF$start, rho=rho_est)summary(phar.gam.AR)
Family: gaussian
Link function: identity
Formula:
y ~ context.ord * vowel.ord + s(Fan, bs = "cr", k = 10) +
s(time, bs = "cr", k = 3) + s(Fan, k = 10, bs = "cr",
by = context.ord) + s(Fan, k = 10, bs = "cr", by = vowel.ord) +
s(Fan, k = 10, bs = "cr", by = context.ord:vowel.ord) +
s(time, k = 3, bs = "cr", by = context.ord) + s(time,
k = 3, bs = "cr", by = vowel.ord) + s(time, k = 3,
bs = "cr", by = context.ord:vowel.ord) + ti(Fan, time,
bs = "cr", k = c(10, 3)) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = vowel.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord:vowel.ord) + s(Fan, word,
bs = "fs", k = 10, m = 1) + s(time, word, bs = "fs",
k = 3, m = 1)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.9560 0.6230 57.711 <2e-16 ***
context.ordvelar 1.8550 0.8688 2.135 0.0328 *
context.orduvular 2.3446 1.2999 1.804 0.0713 .
context.ordpharyngealised 0.5531 0.8208 0.674 0.5004
context.ordpharyngeal 0.4110 1.0741 0.383 0.7020
vowel.ordaa 0.8223 0.7951 1.034 0.3011
vowel.orduu 0.7214 0.8147 0.886 0.3759
context.ordvelar:vowel.ordaa -1.0865 1.1455 -0.949 0.3429
context.orduvular:vowel.ordaa -3.5099 1.6458 -2.133 0.0330 *
context.ordpharyngealised:vowel.ordaa -1.4259 1.1032 -1.293 0.1962
context.ordpharyngeal:vowel.ordaa -0.4642 1.2658 -0.367 0.7138
context.ordvelar:vowel.orduu -2.1728 1.1932 -1.821 0.0686 .
context.orduvular:vowel.orduu -2.8293 1.6414 -1.724 0.0848 .
context.ordpharyngealised:vowel.orduu -1.7135 1.0680 -1.604 0.1086
context.ordpharyngeal:vowel.orduu -0.5365 1.4511 -0.370 0.7116
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F
s(Fan) 8.727e+00 8.773e+00 106.945
s(time) 1.000e+00 1.000e+00 16.144
s(Fan):context.ordvelar 7.819e+00 8.017e+00 7.013
s(Fan):context.orduvular 8.453e+00 8.588e+00 16.795
s(Fan):context.ordpharyngealised 8.702e+00 8.766e+00 29.959
s(Fan):context.ordpharyngeal 1.000e+00 1.000e+00 2.152
s(Fan):vowel.ordaa 8.074e+00 8.236e+00 10.654
s(Fan):vowel.orduu 8.283e+00 8.429e+00 16.564
s(Fan):context.ord:vowel.ordplain:ii 6.530e+00 6.801e+00 5.816
s(Fan):context.ord:vowel.ordplain:aa 1.000e+00 1.000e+00 0.178
s(Fan):context.ord:vowel.ordplain:uu 1.417e+00 1.522e+00 0.034
s(Fan):context.ord:vowel.ordvelar:ii 1.072e-03 1.203e-03 0.201
s(Fan):context.ord:vowel.ordvelar:aa 3.195e+00 3.562e+00 1.852
s(Fan):context.ord:vowel.ordvelar:uu 7.573e+00 7.844e+00 6.715
s(Fan):context.ord:vowel.orduvular:ii 2.466e+00 2.762e+00 0.764
s(Fan):context.ord:vowel.orduvular:aa 1.000e+00 1.000e+00 0.035
s(Fan):context.ord:vowel.orduvular:uu 1.174e-03 1.639e-03 0.057
s(Fan):context.ord:vowel.ordpharyngealised:ii 1.001e+00 1.001e+00 1.767
s(Fan):context.ord:vowel.ordpharyngealised:aa 6.074e+00 6.475e+00 2.247
s(Fan):context.ord:vowel.ordpharyngealised:uu 1.829e-04 2.159e-04 0.100
s(Fan):context.ord:vowel.ordpharyngeal:ii 6.070e+00 6.409e+00 3.069
s(Fan):context.ord:vowel.ordpharyngeal:aa 1.000e+00 1.000e+00 0.430
s(Fan):context.ord:vowel.ordpharyngeal:uu 3.653e+00 4.069e+00 2.637
s(time):context.ordvelar 1.806e+00 1.949e+00 9.646
s(time):context.orduvular 1.000e+00 1.000e+00 4.538
s(time):context.ordpharyngealised 1.000e+00 1.001e+00 0.071
s(time):context.ordpharyngeal 1.000e+00 1.001e+00 3.260
s(time):vowel.ordaa 1.000e+00 1.000e+00 1.090
s(time):vowel.orduu 1.000e+00 1.000e+00 0.601
s(time):context.ord:vowel.ordplain:ii 6.525e-01 8.735e-01 2.150
s(time):context.ord:vowel.ordplain:aa 1.000e+00 1.000e+00 8.033
s(time):context.ord:vowel.ordplain:uu 1.004e+00 1.007e+00 0.188
s(time):context.ord:vowel.ordvelar:ii 1.009e-04 1.858e-04 0.041
s(time):context.ord:vowel.ordvelar:aa 6.899e-01 8.872e-01 2.507
s(time):context.ord:vowel.ordvelar:uu 1.000e+00 1.000e+00 0.696
s(time):context.ord:vowel.orduvular:ii 1.550e+00 1.795e+00 0.590
s(time):context.ord:vowel.orduvular:aa 1.428e-04 2.842e-04 0.172
s(time):context.ord:vowel.orduvular:uu 1.000e+00 1.000e+00 0.862
s(time):context.ord:vowel.ordpharyngealised:ii 1.000e+00 1.000e+00 0.410
s(time):context.ord:vowel.ordpharyngealised:aa 1.194e+00 1.349e+00 1.005
s(time):context.ord:vowel.ordpharyngealised:uu 5.927e-01 8.317e-01 1.750
s(time):context.ord:vowel.ordpharyngeal:ii 7.732e-01 9.470e-01 3.600
s(time):context.ord:vowel.ordpharyngeal:aa 1.000e+00 1.000e+00 0.788
s(time):context.ord:vowel.ordpharyngeal:uu 2.452e-04 4.851e-04 0.312
ti(Fan,time) 9.463e+00 1.115e+01 2.005
ti(Fan,time):context.ordvelar 1.119e+01 1.293e+01 3.199
ti(Fan,time):context.orduvular 1.637e+01 1.754e+01 11.003
ti(Fan,time):context.ordpharyngealised 1.503e+01 1.659e+01 5.238
ti(Fan,time):context.ordpharyngeal 5.918e+00 7.189e+00 5.653
ti(Fan,time):vowel.ordaa 1.452e+01 1.602e+01 5.435
ti(Fan,time):vowel.orduu 1.449e+01 1.590e+01 4.598
ti(Fan,time):context.ord:vowel.ordplain:ii 7.211e+00 7.715e+00 8.781
ti(Fan,time):context.ord:vowel.ordplain:aa 4.723e+00 5.693e+00 2.374
ti(Fan,time):context.ord:vowel.ordplain:uu 5.799e+00 6.764e+00 1.192
ti(Fan,time):context.ord:vowel.ordvelar:ii 7.407e+00 9.611e+00 0.963
ti(Fan,time):context.ord:vowel.ordvelar:aa 1.441e+01 1.625e+01 4.726
ti(Fan,time):context.ord:vowel.ordvelar:uu 3.930e+00 4.755e+00 1.266
ti(Fan,time):context.ord:vowel.orduvular:ii 1.000e+00 1.001e+00 0.068
ti(Fan,time):context.ord:vowel.orduvular:aa 1.000e+00 1.000e+00 0.166
ti(Fan,time):context.ord:vowel.orduvular:uu 9.215e+00 1.250e+01 1.184
ti(Fan,time):context.ord:vowel.ordpharyngealised:ii 1.638e+01 1.744e+01 14.942
ti(Fan,time):context.ord:vowel.ordpharyngealised:aa 1.088e+00 1.446e+00 0.249
ti(Fan,time):context.ord:vowel.ordpharyngealised:uu 1.351e+00 1.535e+00 0.146
ti(Fan,time):context.ord:vowel.ordpharyngeal:ii 1.396e+01 1.582e+01 4.488
ti(Fan,time):context.ord:vowel.ordpharyngeal:aa 9.527e+00 1.227e+01 2.111
ti(Fan,time):context.ord:vowel.ordpharyngeal:uu 8.056e+00 8.616e+00 8.547
s(Fan,word) 3.830e+02 4.850e+02 13.083
s(time,word) 1.522e+01 1.200e+02 0.216
p-value
s(Fan) < 2e-16 ***
s(time) 5.89e-05 ***
s(Fan):context.ordvelar 3.05e-09 ***
s(Fan):context.orduvular < 2e-16 ***
s(Fan):context.ordpharyngealised < 2e-16 ***
s(Fan):context.ordpharyngeal 0.142344
s(Fan):vowel.ordaa 1.58e-15 ***
s(Fan):vowel.orduu < 2e-16 ***
s(Fan):context.ord:vowel.ordplain:ii 2.40e-06 ***
s(Fan):context.ord:vowel.ordplain:aa 0.673336
s(Fan):context.ord:vowel.ordplain:uu 0.898997
s(Fan):context.ord:vowel.ordvelar:ii 0.987583
s(Fan):context.ord:vowel.ordvelar:aa 0.098383 .
s(Fan):context.ord:vowel.ordvelar:uu 1.91e-08 ***
s(Fan):context.ord:vowel.orduvular:ii 0.419912
s(Fan):context.ord:vowel.orduvular:aa 0.850685
s(Fan):context.ord:vowel.orduvular:uu 0.992284
s(Fan):context.ord:vowel.ordpharyngealised:ii 0.183516
s(Fan):context.ord:vowel.ordpharyngealised:aa 0.025824 *
s(Fan):context.ord:vowel.ordpharyngealised:uu 0.996288
s(Fan):context.ord:vowel.ordpharyngeal:ii 0.003906 **
s(Fan):context.ord:vowel.ordpharyngeal:aa 0.512159
s(Fan):context.ord:vowel.ordpharyngeal:uu 0.027386 *
s(time):context.ordvelar 0.000441 ***
s(time):context.orduvular 0.033167 *
s(time):context.ordpharyngealised 0.789879
s(time):context.ordpharyngeal 0.071024 .
s(time):vowel.ordaa 0.296471
s(time):vowel.orduu 0.438021
s(time):context.ord:vowel.ordplain:ii 0.170573
s(time):context.ord:vowel.ordplain:aa 0.004598 **
s(time):context.ord:vowel.ordplain:uu 0.668764
s(time):context.ord:vowel.ordvelar:ii 0.997811
s(time):context.ord:vowel.ordvelar:aa 0.135861
s(time):context.ord:vowel.ordvelar:uu 0.404201
s(time):context.ord:vowel.orduvular:ii 0.420549
s(time):context.ord:vowel.orduvular:aa 0.994423
s(time):context.ord:vowel.orduvular:uu 0.353279
s(time):context.ord:vowel.ordpharyngealised:ii 0.522121
s(time):context.ord:vowel.ordpharyngealised:aa 0.256311
s(time):context.ord:vowel.ordpharyngealised:uu 0.227697
s(time):context.ord:vowel.ordpharyngeal:ii 0.064837 .
s(time):context.ord:vowel.ordpharyngeal:aa 0.374830
s(time):context.ord:vowel.ordpharyngeal:uu 0.990186
ti(Fan,time) 0.019633 *
ti(Fan,time):context.ordvelar 0.000107 ***
ti(Fan,time):context.orduvular < 2e-16 ***
ti(Fan,time):context.ordpharyngealised 1.44e-10 ***
ti(Fan,time):context.ordpharyngeal 1.38e-06 ***
ti(Fan,time):vowel.ordaa 7.44e-12 ***
ti(Fan,time):vowel.orduu 2.20e-09 ***
ti(Fan,time):context.ord:vowel.ordplain:ii 7.96e-12 ***
ti(Fan,time):context.ord:vowel.ordplain:aa 0.033443 *
ti(Fan,time):context.ord:vowel.ordplain:uu 0.327504
ti(Fan,time):context.ord:vowel.ordvelar:ii 0.467691
ti(Fan,time):context.ord:vowel.ordvelar:aa 4.93e-10 ***
ti(Fan,time):context.ord:vowel.ordvelar:uu 0.213464
ti(Fan,time):context.ord:vowel.orduvular:ii 0.794583
ti(Fan,time):context.ord:vowel.orduvular:aa 0.684143
ti(Fan,time):context.ord:vowel.orduvular:uu 0.296132
ti(Fan,time):context.ord:vowel.ordpharyngealised:ii < 2e-16 ***
ti(Fan,time):context.ord:vowel.ordpharyngealised:aa 0.706603
ti(Fan,time):context.ord:vowel.ordpharyngealised:uu 0.756240
ti(Fan,time):context.ord:vowel.ordpharyngeal:ii 4.62e-09 ***
ti(Fan,time):context.ord:vowel.ordpharyngeal:aa 0.012872 *
ti(Fan,time):context.ord:vowel.ordpharyngeal:uu 2.36e-12 ***
s(Fan,word) < 2e-16 ***
s(time,word) < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Rank: 1282/1303
R-sq.(adj) = 0.984 Deviance explained = 98.5%
fREML = 12790 Scale est. = 1.6966 n = 189003.4.1 ACF second GAM
The following five plots show (in the same order as above) the autocorrelation in the residuals. As can be seen, for all five plots, the levels of autocorrelations in the residuals are reduced massively. We have now more confidence in our estimates, and can continue work
3.4.1.1 ACF by Fan
acf_resid(phar.gam.AR,split_pred=list(pharDF$Fan),main = "Average ACF AR by Fan",cex.lab=1.5,cex.axis=1.5)
3.4.1.2 ACF by Time
acf_resid(phar.gam.AR,split_pred=list(pharDF$time),main = "Average ACF AR by Time",cex.lab=1.5,cex.axis=1.5)
3.4.1.3 ACF by Context
acf_resid(phar.gam.AR,split_pred=list(pharDF$context.ord),main = "Average ACF AR by Context",cex.lab=1.5,cex.axis=1.5)
3.4.1.4 ACF by Vowel
acf_resid(phar.gam.AR,split_pred=list(pharDF$vowel.ord),main = "Average ACF AR by Vowel",cex.lab=1.5,cex.axis=1.5)
3.4.1.5 ACF by Word
acf_resid(phar.gam.AR,split_pred=list(pharDF$word),main = "Average ACF AR by Word",cex.lab=1.5,cex.axis=1.5)
3.5 Significance testing second Autoregressive GAM
To test for significance of context, we run a model with a ML as method and evaluate significance through a maximum likelihood estimate.
3.5.1 Models
We ran three models
- A full model with all predictors (phar.gam.AR.ML)
- A reduced model without any terms associated with the predictor “context” (phar.gam.AR.ML.Min.Context)
- An intercept only model (=Null) without any terms associated with the predictor “vowel” (phar.gam.AR.ML.Min.ContextAndVowel)
phar.gam.AR.ML <- bam(y ~ context.ord*vowel.ord +
# 1d smooths
s(Fan, bs="cr",k=10) +
s(time, bs="cr", k=3) +
# 1d smooth * factor
s(Fan, k=10, bs="cr", by=context.ord) +
s(Fan, k=10, bs="cr", by=vowel.ord) +
s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
s(time, k=3, bs="cr", by=context.ord) +
s(time, k=3, bs="cr", by=vowel.ord) +
s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
# 2d smooth
ti(Fan, time, bs="cr", k=c(10,3))+
ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
# random smooths by word
s(Fan, word, bs="fs", k=10, m=1) +
s(time, word, bs="fs", k=3, m=1),
data=pharDF,
method="ML",
AR.start=pharDF$start, rho=rho_est)
phar.gam.AR.ML.Min.Context <- bam(y ~ vowel.ord +
# 1d smooths
s(Fan, bs="cr",k=10) +
s(time, bs="cr", k=3) +
# 1d smooth * factor
##s(Fan, k=10, bs="cr", by=context.ord) +
s(Fan, k=10, bs="cr", by=vowel.ord) +
##s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
##s(time, k=3, bs="cr", by=context.ord) +
s(time, k=3, bs="cr", by=vowel.ord) +
##s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
# 2d smooth
ti(Fan, time, bs="cr", k=c(10,3))+
##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
###ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
# random smooths by word
s(Fan, word, bs="fs", k=10, m=1) +
s(time, word, bs="fs", k=3, m=1),
data=pharDF,
method="ML",
AR.start=pharDF$start, rho=rho_est)
phar.gam.AR.ML.Min.ContextAndVowel <- bam(y ~ 1 +
# 1d smooths
s(Fan, bs="cr",k=10) +
s(time, bs="cr", k=3) +
# 1d smooth * factor
##s(Fan, k=10, bs="cr", by=context.ord) +
###s(Fan, k=10, bs="cr", by=vowel.ord) +
##s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
##s(time, k=3, bs="cr", by=context.ord) +
###s(time, k=3, bs="cr", by=vowel.ord) +
##s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
# 2d smooth
##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
###ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
# random smooths by word
s(Fan, word, bs="fs", k=10, m=1) +
s(time, word, bs="fs", k=3, m=1),
data=pharDF,
method="ML",
AR.start=pharDF$start, rho=rho_est)3.5.2 Model comparison
Then we compare results of these models as below shown below.
3.5.2.1 Effect of Context vs null
compareML(phar.gam.AR.ML, phar.gam.AR.ML.Min.Context)phar.gam.AR.ML: y ~ context.ord * vowel.ord + s(Fan, bs = "cr", k = 10) + s(time,
bs = "cr", k = 3) + s(Fan, k = 10, bs = "cr", by = context.ord) +
s(Fan, k = 10, bs = "cr", by = vowel.ord) + s(Fan, k = 10,
bs = "cr", by = context.ord:vowel.ord) + s(time, k = 3, bs = "cr",
by = context.ord) + s(time, k = 3, bs = "cr", by = vowel.ord) +
s(time, k = 3, bs = "cr", by = context.ord:vowel.ord) + ti(Fan,
time, bs = "cr", k = c(10, 3)) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = vowel.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord:vowel.ord) + s(Fan, word,
bs = "fs", k = 10, m = 1) + s(time, word, bs = "fs", k = 3,
m = 1)
phar.gam.AR.ML.Min.Context: y ~ vowel.ord + s(Fan, bs = "cr", k = 10) + s(time, bs = "cr",
k = 3) + s(Fan, k = 10, bs = "cr", by = vowel.ord) + s(time,
k = 3, bs = "cr", by = vowel.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3)) + s(Fan, word, bs = "fs", k = 10, m = 1) +
s(time, word, bs = "fs", k = 3, m = 1)
Chi-square test of ML scores
-----
AIC difference: -2716.25, model phar.gam.AR.ML has lower AIC.3.5.2.2 Effect of Vowel vs Null
compareML(phar.gam.AR.ML,phar.gam.AR.ML.Min.ContextAndVowel)phar.gam.AR.ML: y ~ context.ord * vowel.ord + s(Fan, bs = "cr", k = 10) + s(time,
bs = "cr", k = 3) + s(Fan, k = 10, bs = "cr", by = context.ord) +
s(Fan, k = 10, bs = "cr", by = vowel.ord) + s(Fan, k = 10,
bs = "cr", by = context.ord:vowel.ord) + s(time, k = 3, bs = "cr",
by = context.ord) + s(time, k = 3, bs = "cr", by = vowel.ord) +
s(time, k = 3, bs = "cr", by = context.ord:vowel.ord) + ti(Fan,
time, bs = "cr", k = c(10, 3)) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = vowel.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3), by = context.ord:vowel.ord) + s(Fan, word,
bs = "fs", k = 10, m = 1) + s(time, word, bs = "fs", k = 3,
m = 1)
phar.gam.AR.ML.Min.ContextAndVowel: y ~ 1 + s(Fan, bs = "cr", k = 10) + s(time, bs = "cr", k = 3) +
s(Fan, word, bs = "fs", k = 10, m = 1) + s(time, word, bs = "fs",
k = 3, m = 1)
Chi-square test of ML scores
-----
AIC difference: -3032.77, model phar.gam.AR.ML has lower AIC.3.5.2.3 Effect of Context vs Vowel
compareML(phar.gam.AR.ML.Min.Context,phar.gam.AR.ML.Min.ContextAndVowel)phar.gam.AR.ML.Min.Context: y ~ vowel.ord + s(Fan, bs = "cr", k = 10) + s(time, bs = "cr",
k = 3) + s(Fan, k = 10, bs = "cr", by = vowel.ord) + s(time,
k = 3, bs = "cr", by = vowel.ord) + ti(Fan, time, bs = "cr",
k = c(10, 3)) + s(Fan, word, bs = "fs", k = 10, m = 1) +
s(time, word, bs = "fs", k = 3, m = 1)
phar.gam.AR.ML.Min.ContextAndVowel: y ~ 1 + s(Fan, bs = "cr", k = 10) + s(time, bs = "cr", k = 3) +
s(Fan, word, bs = "fs", k = 10, m = 1) + s(time, word, bs = "fs",
k = 3, m = 1)
Chi-square test of ML scores
-----
AIC difference: -316.52, model phar.gam.AR.ML.Min.Context has lower AIC.From the results above, both “context” and “vowel” are improving the model fit. “Context” is improving the model fit more than “vowel” (see 3rd comparison)
3.6 Visualising smooths from second Autoregressive GAM
We use the function itsadug::plot_smooth to plot the five smooths for context “plain”, “velar”, “uvular”, “pharyngealised” and “pharyngeal”, in each of the vowel contexts /i: a: u:/ at the onset, midpoint and offset.
The results show that at the midpoint and offset, the three contexts, uvular, pharyngealised and pharyngeal are different from each other: uvular has a higher tongue position (y coordinate); intermediate in pharyngealised and lower and fronted in pharyngeal. At the onset, the same pattern is see though to a lower degree in /a:/.
3.6.1 Vowel /i:/
3.6.1.1 Onset
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="ii",time=1),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /i:/ at Onset",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="ii",time=1),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="ii",time=1),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="ii",time=1),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="ii",time=1),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.1.2 Midpoint
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="ii",time=2),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /i:/ at Mid",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="ii",time=2),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="ii",time=2),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="ii",time=2),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="ii",time=2),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.1.3 Offset
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="ii",time=3),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /i:/ at Offset",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="ii",time=3),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="ii",time=3),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="ii",time=3),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="ii",time=3),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.2 Vowel /a:/
3.6.2.1 Onset
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="aa",time=1),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /a:/ at Onset",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="aa",time=1),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="aa",time=1),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="aa",time=1),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="aa",time=1),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.2.2 Midpoint
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="aa",time=2),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /a:/ at Mid",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="aa",time=2),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="aa",time=2),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="aa",time=2),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="aa",time=2),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.2.3 Offset
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="aa",time=3),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /a:/ at Offset",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="aa",time=3),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="aa",time=3),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="aa",time=3),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="aa",time=3),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.3 Vowel /u:/
3.6.3.1 Onset
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="uu",time=1),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /u:/ at Onset",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="uu",time=1),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="uu",time=1),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="uu",time=1),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="uu",time=1),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.3.2 Midpoint
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="uu",time=2),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /u:/ at Mid",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="uu",time=2),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="uu",time=2),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="uu",time=2),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="uu",time=2),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.6.3.3 Offset
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="uu",time=3),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
main="GAM smooths in /u:/ at Offset",hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): plain.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="uu",time=3),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): velar.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="uu",time=3),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): uvular.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="uu",time=3),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngealised.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="uu",time=3),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)Summary:
* context.ord : factor; set to the value(s): pharyngeal.
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 30 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"),
col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
3.7 Visualising Difference smooths from second Autoregressive GAM
We use the function itsadug::plot_diff to plot the differences between the following pairs: “pharyngealised” vs “plain”; “uvular” vs “pharyngealised” and “pharyngealised” vs “pharyngeal”, in each of the vowel contexts /i: a: u:/ at the onset, midpoint and offset.
The results show that:
- Pharyngealised vs Plain: pharyngealised is different in tongue shape. There is an increase in y coordinates (Fan 15-22) indicating “retraction”, decrease (Fan 25-35) indicating “depression” of the tongue and potential difference in larynx height (Fan 5-10)
- Uvular vs Pharyngealised: differences depend of vowels (Fan 15-34): uvular is fronter in /i:/ (Fan 22-34); “raised” in /a:/ (Fan 22-28) and /u:/ (Fan 15-25)
- Pharyngealised vs Pharyngeal: Pharyngealised is “retracted”/backed to mid-position (Fan 15-25), with “depression” of the tongue (Fan 22-35) and potential lowered larynx (Fan 5-10).
3.7.1 Pharyngealised vs Plain
3.7.1.1 Onset
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=1,vowel.ord="ii"),main = "difference pharyngealised vs plain Onset",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
12.181818 - 22.121212
26.676768 - 39.101010plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=1,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
12.595960 - 21.292929
25.434343 - 35.787879plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=1,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
3.070707 - 8.868687
15.909091 - 22.535354
26.262626 - 38.272727legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.1.2 Midpoint
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=2,vowel.ord="ii"),main = "difference pharyngealised vs plain Midpoint",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
5.141414 - 23.777778
26.262626 - 41.585859plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=2,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
15.080808 - 22.535354
25.848485 - 36.616162plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=2,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
3.484848 - 8.868687
15.909091 - 23.363636
26.676768 - 39.929293legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.1.3 Offset
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=3,vowel.ord="ii"),main = "difference pharyngealised vs plain Offset",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
6.797980 - 24.606061
27.090909 - 41.585859plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=3,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
1.000000 - 7.626263
15.909091 - 22.121212
26.262626 - 35.373737plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
xlab="",cond=list(time=3,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
15.080808 - 23.363636
27.090909 - 39.101010legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.2 Uvular vs Pharyngealised
3.7.2.1 Onset
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=1,vowel.ord="ii"),main = "difference uvular vs pharyngealised Onset",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Difference is not significant.plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=1,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Difference is not significant.plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=1,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Difference is not significant.legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.2.2 Midpoint
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=2,vowel.ord="ii"),main = "difference uvular vs pharyngealised Midpoint",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
20.050505 - 30.404040plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=2,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
20.464646 - 25.848485
34.131313 - 39.101010plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=2,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
21.292929 - 26.262626legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.2.3 Offset
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=3,vowel.ord="ii"),main = "difference uvular vs pharyngealised Offset",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
22.949495 - 32.060606plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=3,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
22.535354 - 27.505051plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
xlab="",cond=list(time=3,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
22.535354 - 26.676768legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.3 Pharyngealised vs Pharyngeal
3.7.3.1 Onset
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=1,vowel.ord="ii"),main = "difference pharyngealised vs pharyngeal Onset",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
15.909091 - 25.434343plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=1,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
13.010101 - 22.535354
26.262626 - 36.202020plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=1,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 1.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
15.909091 - 20.464646
28.333333 - 30.404040legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.3.2 Midpoint
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=2,vowel.ord="ii"),main = "difference pharyngealised vs pharyngeal Midpoint",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
1.414141 - 8.868687
15.909091 - 25.434343
30.818182 - 38.272727plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=2,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
4.313131 - 5.141414
15.909091 - 22.121212
26.262626 - 33.303030plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=2,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 2.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
1.000000 - 13.010101
17.979798 - 23.777778legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
3.7.3.3 Offset
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=3,vowel.ord="ii"),main = "difference pharyngealised vs pharyngeal Offset",
col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): ii.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
15.080808 - 25.848485
29.575758 - 39.515152plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=3,vowel.ord="aa"),add=TRUE,
col='red',mark.diff = TRUE,col.diff = "red",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): aa.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
17.565657 - 20.464646
25.848485 - 33.303030plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
xlab="",cond=list(time=3,vowel.ord="uu"),add=TRUE,
col='blue',mark.diff = TRUE,col.diff = "blue",
ylim=c(-15,15),hide.label=T,rm.ranef=T)Summary:
* vowel.ord : factor; set to the value(s): uu.
* Fan : numeric predictor; with 100 values ranging from 1.000000 to 42.000000.
* time : numeric predictor; set to the value(s): 3.
* word : factor; set to the value(s): 2aa3aa. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(Fan,word),s(time,word)
Fan window(s) of significant difference(s):
1.000000 - 13.838384
18.808081 - 24.191919legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1,
legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
---
title: "GAMMs LabPhon 18"
author: 
  name: "Jalal Al-Tamimi"
  affiliation: "Newcastle University"
date: "6 June 2018"
output: 
  html_notebook:
    number_sections: true
    toc: true
    toc_depth: 4
    toc_float:
      collapsed: true
---

# Loading packages 
```{r warning=FALSE, message=FALSE, error=FALSE}

library(ggplot2);library(mgcv);library(itsadug);library(tidyverse);library(gganimate)
```

# Demo Linear Model vs GAM
In this demo, data obtained from a Jordanian Arabic speaker producing the word /du:d/ "worm" is used. F2 frequencies obtained at 5 ms interval from onset to midpoint of the vowel are used.
Let's plot the F2 frequencies by interval number. The second plot displays a linear regression line superimposed on the plot. As can be seen, the linear regression line is not able to cope with the curviness of the data, and is unable to cope with it. The third plot superimposes a spline obtained from a GAM model.

```{r}
demoGAMProd <- read.csv("dataProd_duud.csv")
head(demoGAMProd)
```


```{r warning=FALSE, message=FALSE, error=FALSE}
ggplot.demo1 <- ggplot(demoGAMProd,aes(x=IntervalsN,y=F2))+
                geom_point()+theme_bw(base_size = 20)+coord_cartesian(ylim=c(600,1800))+
                labs(y = "F2 Hz", x = "Intervals number")
ggplot.demo1

ggplot.demo2 <- ggplot(demoGAMProd,aes(x=IntervalsN,y=F2))+
  geom_point()+theme_bw(base_size = 20)+coord_cartesian(ylim=c(600,1800))+
  labs(y = "F2 Hz", x = "Intervals number")+geom_smooth(method = lm, se=F)

ggplot.demo2

ggplot.demo3 <- ggplot(demoGAMProd,aes(x=IntervalsN,y=predict(bam(F2 ~ s(IntervalsN, bs="cr"), data = demoGAMProd))))+
  geom_point()+theme_bw(base_size = 20)+coord_cartesian(ylim=c(600,1800))+
  labs(y = "F2 Hz", x = "Intervals number")+geom_smooth(method = lm, formula = y ~ splines::bs(x, 3),se=F)

ggplot.demo3

```

# Actual data analyses
This data is obtained from a single speaker producing various consonants in the frame VCV, with V = symmetric /i: a: u:/ (=vowel) and C is one of 17 consonants put into five contexts "plain", "velar", "uvular", "pharyngealised" and "pharyngeal" (=context). The data is from Ultrasound Tongue Imaging. Tongue contours were obtained at the consonant onset, midpoint and offset (=time). A total of 51 words (=word) were obtained and these were repeated 3 times. Tongue splines were extracted with X-Y coordinates (=y outcome). 42 Fan positions (=Fan) were extracted and these are used as a normalisation between contexts.
The outcome is y, the predictors are Fan, time, context and vowel, and the random factor is word.

## Creating new variables
Before running the actual GAM model on the data, we need to:

1. Create two new variables for context and vowel. These will be "ordered" predictors and sum coded
2. To run an Auto-Regressive Model, we need to find the autocorrelation in the dataframe:
  + We rearrange the data by time, ID, and Fan 
  + We add a new variable "start" that marks the start of Fan
  

```{r warning=FALSE, message=FALSE, error=FALSE}
pharDF <- read.csv("resultsFull.c_NoRelNoz.csv")
str(pharDF)

levels(pharDF$context)
pharDF$context <- factor(pharDF$context, 
                                levels = c("plain","velar","uvular","pharyngealised","pharyngeal"))
levels(pharDF$vowel)
pharDF$vowel <- factor(pharDF$vowel, 
                              levels = c("ii","aa","uu"))
levels(pharDF$labelNew)
pharDF$labelNew <- factor(pharDF$labelNew, 
                                 levels = c("CL_Ons","CL_Mid","CL_Off"))


pharDF$ID <- as.numeric(pharDF$ID)
pharDF$Fan <- as.numeric(pharDF$Fan)
pharDF$time <- pharDF$labelNew2


pharDF$context.ord <- as.ordered(pharDF$context)
contrasts(pharDF$context.ord) <- "contr.treatment"

pharDF$vowel.ord <- as.ordered(pharDF$vowel)
contrasts(pharDF$vowel.ord) <- "contr.treatment"

## to find autocorrelation in the data. Rearrange dataframe and add a variable "start" to indicate 
## when Fan == 1
pharDF <- arrange(pharDF, time, ID, Fan)
pharDF$start <- pharDF$Fan==1

```
## Visualising the data

Before running anything, we start by visualising the data and use an animated figure

```{r warning=FALSE, message=FALSE, error=FALSE}
pl <- ggplot(pharDF, aes(x=Fan, y=y, col=context, frame=time)) +
  facet_grid(vowel ~ context) +
  geom_line(aes(group=ID))+theme_bw()
gganimate(pl, ani.width=800, height=300)

```


## Running our first GAM model
This is our first GAM model. This will be used to find the level of autocorrelation in the data. Our model has double non-linear predictors (fan and time), two factor predictors (context and vowel) and one random effect (word). Interactions between fan and time are used. In addition, by context*vowel interaction is added to account for the two-way interaction with respect to each of fan and time.
The function itsadug::acf_resid is used, and we display the level of autocorrelation for each of the "Fan", the "time", the "context", the "vowel" finally the "word"

```{r warning=FALSE, message=FALSE, error=FALSE}

phar.gam.noAR <- bam(y ~ context.ord*vowel.ord +
                        ## 1d smooths
                        s(Fan, bs="cr", k=10) +
                        s(time, bs="cr", k=3) +
                        ## 1d smooths * factors
                        s(Fan, k=10, bs="cr", by=context.ord) +
                        s(Fan, k=10, bs="cr", by=vowel.ord) +
                        s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
                        s(time, k=3, bs="cr", by=context.ord) +
                        s(time, k=3, bs="cr", by=vowel.ord) +
                        s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
                        ## 2d smooths
                        ti(Fan, time, bs="cr", k=c(10,3))+
                        ## 2d smooths * factors
                        ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
                        ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
                        ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
                        ## random smooths by word
                        s(Fan, word, bs="fs", k=10, m=1) +
                        s(time, word, bs="fs", k=3, m=1),
                        data=pharDF)
```

```{r warning=FALSE, message=FALSE, error=FALSE}
summary(phar.gam.noAR)

rho_est <- start_value_rho(phar.gam.noAR)
rho_est
```

### ACF first GAM
The five plots below show the autocorrelation in the residuals. As can be see, the first plot using Fan, has a complete zero correlations. It is possible that because the various fan points obtained alongside the tongue contour are already correlated with each other, and hence the correlations between fan 1 to 2, 2 to 3, 41 to 42 are already taken into account by the model. 
The four additional plots show the autocorrelation in the residuals for time, context, vowel and word. All of these predictors are showing autocorrelations between their successive data points. This means that for a specific time (onset, midpoint or offset), there is already correlations between tongue splines obtained at the onset, the midpoint and the offset. It is crucial to tell the model to account for this, otherwise there is overconfidence in the estimates. 

#### ACF by Fan

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.noAR,split_pred=list(pharDF$Fan),main = "Average ACF No.AR by Fan",cex.lab=1.5,cex.axis=1.5)
```

#### ACF by Time

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.noAR,split_pred=list(pharDF$time),main = "Average ACF No.AR by Time",cex.lab=1.5,cex.axis=1.5)
```

#### ACF by Context

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.noAR,split_pred=list(pharDF$context.ord),main = "Average ACF No.AR by Context",cex.lab=1.5,cex.axis=1.5)
```

#### ACF by Vowel

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.noAR,split_pred=list(pharDF$vowel.ord),main = "Average ACF No.AR by Vowel",cex.lab=1.5,cex.axis=1.5)
```

#### ACF by Word

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.noAR,split_pred=list(pharDF$word),main = "Average ACF No.AR by Word",cex.lab=1.5,cex.axis=1.5)
```


## Running our second Autoregressive GAM

Our second GAM model is identical to the first, but takes into account the autocorrelation. By including AR.start and rho, we are using information obtained from original model to account for the autocorrelation
```{r warning=FALSE, message=FALSE, error=FALSE}
phar.gam.AR <- bam(y ~ context.ord*vowel.ord +
                       ## 1d smooths
                       s(Fan, bs="cr", k=10) +
                       s(time, bs="cr", k=3) +
                       ## 1d smooths * factors
                       s(Fan, k=10, bs="cr", by=context.ord) +
                       s(Fan, k=10, bs="cr", by=vowel.ord) +
                       s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
                       s(time, k=3, bs="cr", by=context.ord) +
                       s(time, k=3, bs="cr", by=vowel.ord) +
                       s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
                       ## 2d smooths
                       ti(Fan, time, bs="cr", k=c(10,3))+
                       ## 2d smooths * factors
                       ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
                       ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
                       ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+
                       ## random smooths by word
                       s(Fan, word, bs="fs", k=10, m=1) +
                       s(time, word, bs="fs", k=3, m=1),
                       data=pharDF,
                       AR.start=pharDF$start, rho=rho_est)
```

```{r warning=FALSE, message=FALSE, error=FALSE}
summary(phar.gam.AR)
```

### ACF second GAM
The following five plots show (in the same order as above) the autocorrelation in the residuals. As can be seen, for all five plots, the levels of autocorrelations in the residuals are reduced massively. We have now more confidence in our estimates, and can continue work

#### ACF by Fan

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.AR,split_pred=list(pharDF$Fan),main = "Average ACF AR by Fan",cex.lab=1.5,cex.axis=1.5)
```


#### ACF by Time

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.AR,split_pred=list(pharDF$time),main = "Average ACF AR by Time",cex.lab=1.5,cex.axis=1.5)
```

#### ACF by Context

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.AR,split_pred=list(pharDF$context.ord),main = "Average ACF AR by Context",cex.lab=1.5,cex.axis=1.5)
```


#### ACF by Vowel

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.AR,split_pred=list(pharDF$vowel.ord),main = "Average ACF AR by Vowel",cex.lab=1.5,cex.axis=1.5)
```

#### ACF by Word

```{r warning=FALSE, message=FALSE, error=FALSE}
acf_resid(phar.gam.AR,split_pred=list(pharDF$word),main = "Average ACF AR by Word",cex.lab=1.5,cex.axis=1.5)
```


## Significance testing second Autoregressive GAM
To test for significance of context, we run a model with a ML as method and evaluate significance through a maximum likelihood estimate. 

### Models
We ran three models

1. A full model with all predictors (phar.gam.AR.ML)
2. A reduced model without any terms associated with the predictor "context" (phar.gam.AR.ML.Min.Context)
3. An intercept only model (=Null) without any terms associated with the predictor "vowel" (phar.gam.AR.ML.Min.ContextAndVowel)

```{r warning=FALSE, message=FALSE, error=FALSE}
phar.gam.AR.ML <- bam(y ~ context.ord*vowel.ord +
                           # 1d smooths
                           s(Fan, bs="cr",k=10) +
                           s(time, bs="cr", k=3) +
                           # 1d smooth * factor
                           s(Fan, k=10, bs="cr", by=context.ord) +
                           s(Fan, k=10, bs="cr", by=vowel.ord) +
                           s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
                           s(time, k=3, bs="cr", by=context.ord) +
                           s(time, k=3, bs="cr", by=vowel.ord) +
                           s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
                           # 2d smooth
                           ti(Fan, time, bs="cr", k=c(10,3))+
                           ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
                           ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
                           ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+                      
                           # random smooths by word
                           s(Fan, word, bs="fs", k=10, m=1) +
                           s(time, word, bs="fs", k=3, m=1),
                           data=pharDF,
                           method="ML", 
                           AR.start=pharDF$start, rho=rho_est)

phar.gam.AR.ML.Min.Context <- bam(y ~ vowel.ord +
                                      # 1d smooths
                                      s(Fan, bs="cr",k=10) +
                                      s(time, bs="cr", k=3) +
                                      # 1d smooth * factor
                                      ##s(Fan, k=10, bs="cr", by=context.ord) +
                                      s(Fan, k=10, bs="cr", by=vowel.ord) +
                                      ##s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
                                      ##s(time, k=3, bs="cr", by=context.ord) +
                                      s(time, k=3, bs="cr", by=vowel.ord) +
                                      ##s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
                                      # 2d smooth
                                      ti(Fan, time, bs="cr", k=c(10,3))+
                                      ##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
                                      ###ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
                                      ##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+                      
                                      # random smooths by word
                                      s(Fan, word, bs="fs", k=10, m=1) +
                                      s(time, word, bs="fs", k=3, m=1),
                                      data=pharDF,
                                      method="ML", 
                                      AR.start=pharDF$start, rho=rho_est)


phar.gam.AR.ML.Min.ContextAndVowel <- bam(y ~ 1 +
                                    # 1d smooths
                                    s(Fan, bs="cr",k=10) +
                                    s(time, bs="cr", k=3) +
                                    # 1d smooth * factor
                                    ##s(Fan, k=10, bs="cr", by=context.ord) +
                                    ###s(Fan, k=10, bs="cr", by=vowel.ord) +
                                    ##s(Fan, k=10, bs="cr", by=context.ord:vowel.ord) +
                                    ##s(time, k=3, bs="cr", by=context.ord) +
                                    ###s(time, k=3, bs="cr", by=vowel.ord) +
                                    ##s(time, k=3, bs="cr", by=context.ord:vowel.ord) +
                                    # 2d smooth
                                    ##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord)+
                                    ###ti(Fan, time, bs="cr", k=c(10,3),by=vowel.ord)+
                                    ##ti(Fan, time, bs="cr", k=c(10,3),by=context.ord:vowel.ord)+                      
                                    # random smooths by word
                                    s(Fan, word, bs="fs", k=10, m=1) +
                                    s(time, word, bs="fs", k=3, m=1),
                                    data=pharDF,
                                    method="ML", 
                                    AR.start=pharDF$start, rho=rho_est)
```

### Model comparison
Then we compare results of these models as below shown below.    

#### Effect of Context vs null
```{r warning=FALSE, message=FALSE, error=FALSE}
compareML(phar.gam.AR.ML, phar.gam.AR.ML.Min.Context)
```

#### Effect of Vowel vs Null
```{r warning=FALSE, message=FALSE, error=FALSE}
compareML(phar.gam.AR.ML,phar.gam.AR.ML.Min.ContextAndVowel)
```

#### Effect of Context vs Vowel
```{r warning=FALSE, message=FALSE, error=FALSE}
compareML(phar.gam.AR.ML.Min.Context,phar.gam.AR.ML.Min.ContextAndVowel)
```

From the results above, both "context" and "vowel" are improving the model fit. "Context" is improving the model fit more than "vowel" (see 3rd comparison)

## Visualising smooths from second Autoregressive GAM
We use the function itsadug::plot_smooth to plot the five smooths for context "plain", "velar", "uvular", "pharyngealised" and "pharyngeal", in each of the vowel contexts /i: a: u:/ at the onset, midpoint and offset.

The results show that at the midpoint and offset, the three contexts, uvular, pharyngealised and pharyngeal are different from each other: uvular has a higher tongue position (y coordinate); intermediate in pharyngealised and lower and fronted in pharyngeal. At the onset, the same pattern is see though to a lower degree in /a:/.

### Vowel /i:/

#### Onset
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="ii",time=1),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /i:/ at Onset",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="ii",time=1),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="ii",time=1),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="ii",time=1),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="ii",time=1),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

#### Midpoint
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="ii",time=2),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /i:/ at Mid",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="ii",time=2),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="ii",time=2),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="ii",time=2),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="ii",time=2),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

#### Offset
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="ii",time=3),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /i:/ at Offset",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="ii",time=3),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="ii",time=3),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="ii",time=3),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="ii",time=3),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

### Vowel /a:/

#### Onset
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="aa",time=1),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /a:/ at Onset",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="aa",time=1),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="aa",time=1),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="aa",time=1),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="aa",time=1),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

#### Midpoint
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="aa",time=2),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /a:/ at Mid",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="aa",time=2),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="aa",time=2),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="aa",time=2),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="aa",time=2),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

#### Offset
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="aa",time=3),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /a:/ at Offset",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="aa",time=3),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="aa",time=3),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="aa",time=3),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="aa",time=3),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

### Vowel /u:/

#### Onset
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="uu",time=1),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /u:/ at Onset",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="uu",time=1),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="uu",time=1),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="uu",time=1),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="uu",time=1),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

#### Midpoint
```{r warning=FALSE, message=FALSE, error=FALSE}
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="uu",time=2),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /u:/ at Mid",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="uu",time=2),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="uu",time=2),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="uu",time=2),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="uu",time=2),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)
```

#### Offset
```{r warning=FALSE, message=FALSE, error=FALSE}

plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="plain",vowel.ord="uu",time=3),col="darkmagenta",ylim = c(10,65),ylab="",xlab="",
            main="GAM smooths in /u:/ at Offset",hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="velar",vowel.ord="uu",time=3),col="gray70",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="uvular",vowel.ord="uu",time=3),col="green",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngealised",vowel.ord="uu",time=3),col="red",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
plot_smooth(phar.gam.AR, view = "Fan",cond = list(context.ord="pharyngeal",vowel.ord="uu",time=3),col="blue",add=TRUE,ylim = c(10,65),hide.label=T,cex.axis=1.3,rm.ranef=T)
legend("topleft", legend=c("Plain", "Velar", "Uvular","Pharyngealised","Pharyngeal"), 
       col=c("darkmagenta","gray70","green","red","blue"), lwd=4)


```

## Visualising Difference smooths from second Autoregressive GAM
We use the function itsadug::plot_diff to plot the differences between the following pairs: "pharyngealised" vs "plain"; "uvular" vs "pharyngealised" and "pharyngealised" vs "pharyngeal", in each of the vowel contexts /i: a: u:/ at the onset, midpoint and offset.

The results show that: 

1. Pharyngealised vs Plain: pharyngealised is different in tongue shape. There is an increase in y coordinates (Fan 15-22) indicating "retraction", decrease (Fan 25-35) indicating "depression" of the tongue and potential difference in larynx height (Fan 5-10)
2. Uvular vs Pharyngealised: differences depend of vowels (Fan 15-34): uvular is fronter in /i:/ (Fan 22-34); "raised" in /a:/ (Fan 22-28) and /u:/ (Fan 15-25)
3. Pharyngealised vs Pharyngeal: Pharyngealised is "retracted"/backed to mid-position (Fan 15-25), with "depression" of the tongue (Fan 22-35) and potential lowered larynx (Fan 5-10).

### Pharyngealised vs Plain
#### Onset
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=1,vowel.ord="ii"),main = "difference pharyngealised vs plain Onset",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=1,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=1,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```

#### Midpoint
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=2,vowel.ord="ii"),main = "difference pharyngealised vs plain Midpoint",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=2,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=2,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```

#### Offset
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=3,vowel.ord="ii"),main = "difference pharyngealised vs plain Offset",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=3,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","plain")),
          xlab="",cond=list(time=3,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```


### Uvular vs Pharyngealised

#### Onset
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=1,vowel.ord="ii"),main = "difference uvular vs pharyngealised Onset",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=1,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=1,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)

```

#### Midpoint
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=2,vowel.ord="ii"),main = "difference uvular vs pharyngealised Midpoint",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=2,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=2,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```

#### Offset
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=3,vowel.ord="ii"),main = "difference uvular vs pharyngealised Offset",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=3,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("uvular","pharyngealised")),
          xlab="",cond=list(time=3,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```


### Pharyngealised vs Pharyngeal

#### Onset
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=1,vowel.ord="ii"),main = "difference pharyngealised vs pharyngeal Onset",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=1,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=1,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```

#### Midpoint
```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=2,vowel.ord="ii"),main = "difference pharyngealised vs pharyngeal Midpoint",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=2,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=2,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)
```

#### Offset

```{r warning=FALSE, message=FALSE, error=FALSE}
par(oma=c(1, 0, 0, 3.5),mgp=c(2, 1, 0))
plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=3,vowel.ord="ii"),main = "difference pharyngealised vs pharyngeal Offset",
          col='green',cex.main=1.1,mark.diff = TRUE,col.diff = "green",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=3,vowel.ord="aa"),add=TRUE,
          col='red',mark.diff =  TRUE,col.diff = "red",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)

plot_diff(phar.gam.AR, view="Fan",comp=list(context.ord=c("pharyngealised","pharyngeal")),
          xlab="",cond=list(time=3,vowel.ord="uu"),add=TRUE,
          col='blue',mark.diff =  TRUE,col.diff = "blue",
          ylim=c(-15,15),hide.label=T,rm.ranef=T)
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, 
       legend=c("/i:/", "/a:/", "/u:/"),col=c("green","red","blue"), lwd=4, cex=1.2)

```

