CODEWITHSUNDEEP
rstatisticsglm
zombiecalypse
zombiecalypse

Reputation: 63

Interpreting the output of summary(glmer(...)) in R

I'm an R noob, I hope you can help me:

I'm trying to analyse a dataset in R, but I'm not sure how to interpret the output of summary(glmer(...)) and the documentation isn't a big help:

> data_chosen_stim<-glmer(open_chosen_stim~closed_chosen_stim+day+(1|ID),family=binomial,data=chosenMovement)
> summary(data_chosen_stim)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial ( logit )
Formula: open_chosen_stim ~ closed_chosen_stim + day + (1 | ID)
   Data: chosenMovement

     AIC      BIC   logLik deviance df.resid 
    96.7    105.5    -44.4     88.7       62 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.4062 -1.0749  0.7111  0.8787  1.0223 

Random effects:
 Groups Name        Variance Std.Dev.
 ID     (Intercept) 0        0       
Number of obs: 66, groups: ID, 35

Fixed effects:
                    Estimate Std. Error z value Pr(>|z|)
(Intercept)           0.4511     0.8715   0.518    0.605
closed_chosen_stim2   0.4783     0.5047   0.948    0.343
day                  -0.2476     0.5060  -0.489    0.625

Correlation of Fixed Effects:
            (Intr) cls__2
clsd_chsn_2 -0.347       
day         -0.916  0.077

I understand the GLM behind it, but I can't see the weights of the independent variables and their error bounds.

Upvotes: 1

Views: 9099

Answers (1)

Ben Bolker
Ben Bolker

Reputation: 226741

update: weights.merMod already has a type argument ...

I think what you're looking for weights(object,type="working").

I believe these are the diagonal elements of W in your notation?

Here's a trivial example that matches up the results of glm and glmer (since the random effect is bogus and gets an estimated variance of zero, the fixed effects, weights, etc etc converges to the same value).

Note that the weights() accessor returns the prior weights by default (these are all equal to 1 for the example below).

Example (from ?glm):

 d.AD <- data.frame(treatment=gl(3,3),
                    outcome=gl(3,1,9),
                    counts=c(18,17,15,20,10,20,25,13,12))
 glm.D93 <- glm(counts ~ outcome + treatment, family = poisson(),
                data=d.AD)
 library(lme4)
 d.AD$f <- 1  ## dummy grouping variable
 glmer.D93 <- glmer(counts ~ outcome + treatment + (1|f),
                    family = poisson(),
                    data=d.AD,
                    control=glmerControl(check.nlev.gtr.1="ignore"))

Fixed effects and weights are the same:

 all.equal(fixef(glmer.D93),coef(glm.D93))  ## TRUE
 all.equal(unname(weights(glm.D93,type="working")),
                  weights(glmer.D93,type="working"),
           tol=1e-7)   ## TRUE

Upvotes: 0

Related Questions