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)$coefficients
No SE for A level in G category is calculated.
* Here is a synthesis of the answers:
1/ The first solution was proposed by Rolf Turner
<r.turner at auckland.ac.nz>. It was to add a + 0 in the formula and then
it is possible to have the SE for the 4 levels (it works also with
objects obtained with lme4:lmer() ):
m1 <- glm(y ~ x + G +0)
summary(m1)$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