Try summary(glm.object)$coefficients.
-roger
Peter Alspach wrote:
Kia ora list members:
I'm having a little difficulty getting the correct standard errors from a glm.object (R 1.9.0 under Windows XP 5.1). predict() will gives standard errors of the predicted values, but I am wanting the standard errors of the mean.
To clarify:
Assume I have a 4x3x2 factorial with 2 complete replications (i.e. 48 observations, I've appended a dummy set of data at the end of this message). Call the treatments trt1 (4 levels), trt2 (3 levels) and trt3 (2 levels) and the replications rep - all are factors. The observed data is S. Then:
temp.aov <- aov(S~rep+trt1*trt2*trt3, data=dummy.data) model.tables(temp.aov, type='mean', se=T)
Returns the means, but states "Design is unbalanced - use se.contrasts for se's" which is a little surprising since the design is balanced. Nevertheless, se.contrast gives what I'd expect:
se.contrast(temp.aov, list(trt1==0, trt1==1), data=dummy.data) [1] 5.960012
i.e. standard error of mean is 5.960012/sqrt(2) = 4.214, which is the sqrt(anova(temp.aov)[9,3]/12) as expected. Similarly for interactions, e.g.:
se.contrast(temp.aov, list(trt1==0 & trt2==0, trt1==1 & trt2==1), data=dummy.data)/sqrt(2) [1] 7.299494
How do I get the equivalent of these standard errors if I have used lm(), and by extension glm()? I think I should be able to get these using predict(..., type='terms', se=T) or coef(summary()) but can't quite see how.
predict(lm(S~rep+trt1*trt2*trt3, data=dummy.data),
type='terms', se=T) or predict(glm(cbind(S,
100-S)~rep+trt1*trt2*trt3, data=dummy.data,
family='binomial'), type='terms', se=T) or, as in my case, predict(glm(cbind(S, 100-S)~rep+trt1*trt2*trt3,
data=dummy.data, family='quasibinomial'), type='terms', se=T)
Thanks ........
Peter Alspach HortResearch
dummy.data trt1 trt2 trt3 rep S 0 0 0 1 33 0 0 0 2 55 0 0 1 1
18 0 0 1 2 12 0 1 0 1 47 0 1 0 2 16 0 1 1 1 22 0 1 1 2 33 0 2
0 1 22 0 2 0 2 18 0 2 1 1 60 0 2 1 2 40 1 0 0 1 38 1 0 0 2 24 1 0 1 1 8 1 0 1 2 14 1 1 0 1 69 1 1 0 2 42 1 1 1 1 42 1 1 1 2
44 1 2 0 1 48 1 2 0 2 26 1 2 1 1 46 1 2 1 2 33 2 0 0 1 48 2 0
0 2 46 2 0 1 1 24 2 0 1 2 8 2 1 0 1 69 2 1 0 2 33 2 1 1 1 22 2 1 1 2 33 2 2 0 1 33 2 2 0 2 18 2 2 1 1 26 2 2 1 2 42 3 0 0 1
12 3 0 0 2 42 3 0 1 1 16 3 0 1 2 22 3 1 0 1 14 3 1 0 2 60 3 1
1 1 40 3 1 1 2 55 3 2 0 1 47 3 2 0 2 38 3 2 1 1 18 3 2 1 2 44
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