[R] SE for all fixed factor effect in GLMM

[Rolf Turner]

On 1/2/19 9:35 AM, Marc Girondot wrote:
> Hello members of the list,
> 
> I asked 3 days ago a question about "how to get the SE of all effects 
> after a glm or glmm". I post here a synthesis of the answer and a new 
> solution:
> 
> For example:
> 
> x <- rnorm(100)
> 
> y <- rnorm(100)
> 
> G <- as.factor(sample(c("A", "B", "C", "D"), 100, replace = TRUE)); G <- 
> relevel(G, "A")
> 
> 
> m <- glm(y ~ x + G)
> 
> summary(m)$coefficients
> 
> 
> No SE for A level in G category is calculated.
> 
> 
> * Here is a synthesis of the answers:
> 
> 
> 1/ The first solution was proposed by Rolf Turner 
> <r.turner using auckland.ac.nz>. It was to add a + 0 in the formula and then 
> it is possible to have the SE for the 4 levels (it works also with 
> objects obtained with lme4:lmer() ):
> 
> m1 <- glm(y ~ x + G +0)
> 
> summary(m1)$coefficients
> 
> 
> However, this solution using + 0 does not works if more than one 
> category is included. Only the levels of the first one have all the SE 
> estimated.

Well, you only asked about the setting in which there was only one 
categorical predictor.  If there are, e.g. two (say "G" and "H") try

m2 <- glm(y ~ x + G:H + 0)

I would suggest that you learn a bit about how the formula structure 
works in linear models.

cheers,

Rolf Turner

P.S.  Your use of relevel() is redundant/irrelevant in this context.

R. T.

-- 
Honorary Research Fellow
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276