# [R] LMER quasibinomial

Daniel Malter daniel at umd.edu
Sun Oct 26 17:36:10 CET 2008

```Hi,

a while ago I posted a question regarding the use of alternative models,
including a quasibinomial mixed-effects model (see Results 1). I rerun the
exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results
2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3).

While the coefficient estimates are basically the same in all three
regressions, the estimated standard errors and t-values vary dramatically
(also the variance of the random effects) despite running the exact same
model. Is there any advice which of the models to trust and as to where
these differences come from?

Thanks,
Daniel

---------
Results 1
---------
Generalized linear mixed model fit using Laplace
Formula: prob.bind ~ capacity * group + (1 | subject)
Subset: c(combination == "gnl")
AIC   BIC logLik deviance
11082 11109  -5534    11068
Random effects:
Groups   Name        Variance Std.Dev.
subject  (Intercept) 42.977   6.5557
Residual             26.845   5.1813
number of obs: 360, groups: subject, 90

Fixed effects:
Estimate Std. Error t value
(Intercept)      -3.8628     1.2701  -3.041
capacity          1.1219     0.1176   9.542
group2            0.9086     1.7905   0.507
group3            2.3700     1.7936   1.321
capacity:group2  -0.1745     0.1610  -1.083
capacity:group3  -0.3807     0.1622  -2.348

Correlation of Fixed Effects:
(Intr) capcty group2 group3 cpct:2
capacity    -0.322
group2      -0.709  0.228
group3      -0.708  0.228  0.502
capcty:grp2  0.235 -0.730 -0.310 -0.167
capcty:grp3  0.233 -0.725 -0.166 -0.305  0.529

---------
Results 2
---------
Generalized linear mixed model fit by the Laplace approximation
Formula: entryprob.bind ~ capacity * factor(group) + (1 | subject)
Data: res
Subset: c(which(is.na(entryprob) == FALSE) & combination == "gnl")
AIC   BIC logLik deviance
11084 11115  -5534    11068
Random effects:
Groups   Name        Variance Std.Dev.
subject  (Intercept) 0.021575 0.14688
Residual             0.013457 0.11601
Number of obs: 360, groups: subject, 90

Fixed effects:
Estimate Std. Error t value
(Intercept)             -3.864981   0.028454  -135.8
capacity                 1.121713   0.002632   426.1
factor(group)2           0.909167   0.040113    22.7
factor(group)3           2.372638   0.040184    59.0
capacity:factor(group)2 -0.173956   0.003606   -48.2
capacity:factor(group)3 -0.380799   0.003631  -104.9

Correlation of Fixed Effects:
(Intr) capcty fct()2 fct()3 cp:()2
capacity    -0.322
factr(grp)2 -0.709  0.228
factr(grp)3 -0.708  0.228  0.502
cpcty:fc()2  0.235 -0.730 -0.309 -0.166
cpcty:fc()3  0.233 -0.725 -0.165 -0.304  0.529

-----------
Results 3
-----------
Generalized linear mixed model fit by the Laplace approximation
Formula: entryprob.bind ~ capacity * factor(group) + (1 | subject)
Data: res
Subset: c(which(is.na(entryprob) == FALSE) & combination == "gnl")
AIC   BIC logLik deviance
11082 11109  -5534    11068
Random effects:
Groups  Name        Variance Std.Dev.
subject (Intercept) 1.6032   1.2662
Number of obs: 360, groups: subject, 90

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)             -3.86498    0.24528  -15.76  < 2e-16 ***
capacity                 1.12171    0.02269   49.43  < 2e-16 ***
factor(group)2           0.90917    0.34578    2.63  0.00856 **
factor(group)3           2.37264    0.34639    6.85 7.41e-12 ***
capacity:factor(group)2 -0.17396    0.03108   -5.60 2.18e-08 ***
capacity:factor(group)3 -0.38080    0.03130  -12.17  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
(Intr) capcty fct()2 fct()3 cp:()2
capacity    -0.322
factr(grp)2 -0.709  0.228
factr(grp)3 -0.708  0.228  0.502
cpcty:fc()2  0.235 -0.730 -0.309 -0.166
cpcty:fc()3  0.233 -0.725 -0.165 -0.304  0.529

-------------------------
cuncta stricte discussurus

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