```
library(lme4)
<- glmer(Dep.Var ~ After.New +
td.glmer.parsimonious + Before + Stress + Phoneme + (1 | Speaker),
Morph.Type data = td, family = "binomial", control = glmerControl(optCtrl = list(maxfun = 20000),
optimizer = "bobyqa"))
summary(td.glmer.parsimonious)
```

```
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: Dep.Var ~ After.New + Morph.Type + Before + Stress + Phoneme +
(1 | Speaker)
Data: td
Control: glmerControl(optCtrl = list(maxfun = 20000), optimizer = "bobyqa")
AIC BIC logLik deviance df.resid
1114 1175 -545 1090 1177
Scaled residuals:
Min 1Q Median 3Q Max
-5.223 -0.488 -0.259 0.495 14.033
Random effects:
Groups Name Variance Std.Dev.
Speaker (Intercept) 0.796 0.892
Number of obs: 1189, groups: Speaker, 66
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.277 0.207 -1.34 0.18034
After.New1 1.840 0.157 11.71 < 2e-16 ***
After.New2 -1.175 0.144 -8.14 4.1e-16 ***
Morph.Type1 0.426 0.140 3.05 0.00230 **
Morph.Type2 -1.892 0.213 -8.87 < 2e-16 ***
Before1 -0.575 0.202 -2.84 0.00447 **
Before2 0.526 0.193 2.72 0.00659 **
Before3 0.117 0.278 0.42 0.67370
Before4 0.731 0.190 3.85 0.00012 ***
Stress1 -0.799 0.137 -5.81 6.2e-09 ***
Phoneme1 0.287 0.128 2.25 0.02462 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Aft.N1 Aft.N2 Mrp.T1 Mrp.T2 Befor1 Befor2 Befor3 Befor4
After.New1 0.064
After.New2 -0.104 -0.430
Morph.Type1 -0.434 0.203 -0.114
Morph.Type2 -0.051 -0.221 0.178 -0.376
Before1 -0.296 -0.223 0.293 0.052 0.429
Before2 -0.164 0.191 -0.094 -0.110 0.247 0.029
Before3 0.150 0.018 -0.060 0.319 -0.515 -0.421 -0.477
Before4 0.250 0.304 -0.431 -0.202 0.051 -0.311 -0.090 -0.274
Stress1 -0.434 -0.432 -0.064 0.050 0.097 0.056 0.125 -0.094 -0.250
Phoneme1 0.459 0.149 -0.307 -0.137 -0.265 -0.543 -0.263 0.149 0.438
Strss1
After.New1
After.New2
Morph.Type1
Morph.Type2
Before1
Before2
Before3
Before4
Stress1
Phoneme1 -0.107
```