I am working on linguistic data and try to investigate the realisation of the vowel in words such as NURSE. There are more less 3 categories that can be realised, which I coded as <Er, Ir, Vr>. I then measured Formant values (F1 and F2). Then I created an LME that predicts the F1 and F2 values with different fixed and random effects but the main effect is a cross random effect of phoneme (i.e. <Er, Ir, Vr>) and individual. An example model can be found below.
Linear mixed model fit by REML ['lmerMod']
Formula:
F2 ~ (phoneme | individual) + (1 | word) + age + frequency +
(1 | zduration)
Data: nurse_female
REML criterion at convergence: 654.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.09203 -0.20332 0.03263 0.25273 1.37056
Random effects:
Groups Name Variance Std.Dev. Corr
zduration (Intercept) 0.27779 0.5271
word (Intercept) 0.04488 0.2118
individual (Intercept) 0.34181 0.5846
phonemeIr 0.54227 0.7364 -0.82
phonemeVr 1.52090 1.2332 -0.93 0.91
Residual 0.06326 0.2515
Number of obs: 334, groups:
zduration, 280; word, 116; individual, 23
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.79167 0.32138 5.575
age -0.01596 0.00508 -3.142
frequencylow -0.37587 0.18560 -2.025
frequencymid -1.18901 0.27738 -4.286
frequencyvery high -0.68365 0.26564 -2.574
Correlation of Fixed Effects:
(Intr) age frqncyl frqncym
age -0.811
frequencylw -0.531 -0.013
frequencymd -0.333 -0.006 0.589
frqncyvryhg -0.356 0.000 0.627 0.389
The question is now, how would I go about plotting the mean F2 values for each individual and for each of 3 variants <Er, Ir, Vr>?
I tried plotting the random effects as a caterpillar plot and get the following, but I am not sure, if this is accurate or does what I want. If what I have done Is right, are there any other better ways of plotting it?
ranefs_nurse_female_F2 <- ranef(nurse_female_F2.lmer8_2)
dotplot(ranefs_nurse_female_F2)
