[R] extracting coefficients from lmer

[Jonathan Williams]

Dear R-Helpers,

I want to compare the results of outputs from glmmPQL and lmer analyses.
I could do this if I could extract the coefficients and standard errors
from the summaries of the lmer models. This is easy to do for the glmmPQL
summaries, using

> glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df,
	family = binomial), TRUE)
> summary(glmmPQL.fit)$tTable

Linear mixed-effects model fit by maximum likelihood
 Data: df
       AIC      BIC    logLik
  1800.477 1840.391 -890.2384

Random effects:
 Formula: ~1 | subject
        (Intercept)  Residual
StdDev:   0.6355517 0.9650671

Variance function:
 Structure: fixed weights
 Formula: ~invwt
Fixed effects: score ~ x * type
                 Value Std.Error  DF    t-value p-value
(Intercept) -0.0812834 0.2933314 294 -0.2771043  0.7819
x1           0.4143072 0.4180624  98  0.9910176  0.3241
type2        0.8509166 0.4084443 294  2.0833112  0.0381
type3        0.6691275 0.4024369 294  1.6626894  0.0974
type4       -0.7830413 0.4123851 294 -1.8988109  0.0586
x1:type2     1.0643239 0.6791126 294  1.5672274  0.1181
x1:type3    -0.7533085 0.5674532 294 -1.3275253  0.1854
x1:type4    -0.0549616 0.5777216 294 -0.0951351  0.9243
etc.

However, there seems to be no route to extract the corresponding information
from the lmer model:-

> lmer.fit=try(lmer(score~x*type+(1|subject), data=df, family=binomial,
	method='AGQ'),TRUE)
> summary(lmer.fit)

Generalized linear mixed model fit using AGQ
Formula: score ~ x * type + (1 | subject)
   Data: df
 Family: binomial(logit link)
      AIC      BIC    logLik deviance
 510.2616 550.1762 -245.1308 490.2616
Random effects:
     Groups        Name    Variance    Std.Dev.
    subject (Intercept)     0.46269     0.68021
# of obs: 400, groups: subject, 100

Estimated scale (compare to 1)  1.019134

Fixed effects:
             Estimate Std. Error  z value Pr(>|z|)
(Intercept) -0.087284   0.300896 -0.29008  0.77175
x1           0.446289   0.428844  1.04068  0.29803
type2        0.913571   0.418978  2.18047  0.02922 *
type3        0.719023   0.412816  1.74175  0.08155 .
type4       -0.839842   0.423021 -1.98534  0.04711 *
x1:type2     1.112673   0.696629  1.59722  0.11022
x1:type3    -0.809599   0.582089 -1.39085  0.16427
x1:type4    -0.062235   0.592623 -0.10502  0.91636
etc.

> summary(lmer.fit)$tTable
NULL
> names(summary(lmer.fit))
NULL
> names(lmer.fit)
NULL
> lmer.fit$coef
NULL

So, then I tried to find out if lmer returns different information.
> help(lmer)
This says "see lmer-class" ->
> help(lmer-class)
No documentation for 'lmer - class' in specified packages and libraries:
you could try 'help.search("lmer - class")'

So, then I tried
> help.search('lmer-class')

This returns
Help files with alias or concept or title matching 'lmer-class' using fuzzy
matching:
lmer-class(Matrix)               Mixed model representations

So, I loaded library Matrix and tried again
> library(Matrix)
> help(lmer-class)
No documentation for 'lmer - class' in specified packages and libraries:
you could try 'help.search("lmer - class")'

If someone could tell me how to extract the Estimates and Std. Errors from
the lmer summary, I'd be very grateful. I would also be very grateful if
someone
could let me know why the Estimates and Std. Errors from the lmer model are
both
larger than those from the glmmPQL model.

I am running R 2.2.1 on a Windows XP machine.

Thanks,

Jonathan Williams