Hey, all. I had a quick question about fitting new glm values and
then looking at the error around them. I'm working with a glm using a
Gamma distribution and a log link with two types of treatments.
However, when I then look at the predicted values for each category, I
find for the one that is close to 0, the error (using se.fit=T with
predicted) actually makes it overlap 0. This is not possible, as
non-0 values have no meaning.
Am I missing something in the interpretation? I'm sure I am. Is
there are better way to plot this that is accurate? Below is some
sample code for an example problem. Note that the error for the value
for category a (plotted at the end) does cross 0.
Note: this is a simple example. The model I'm using has covariates,
etc, but, I wanted to work out the correct method for the simple
example so that I could take it back to my more complex model. Thanks!
#data
x<-as.factor(c(rep("a",4),rep("b",4)))
y<-c(0.5,2,0.3,1.2,32.6,43,23.8,2.92)
#plot the raw data
plot(y ~ as.factor(x))
#fit a glm
my.glm<-glm(y ~ x, family=Gamma(link=log))
#get predicted values and their error
a.fit<-predict(my.glm, data.frame(x="a"), se.fit=T)
b.fit<-predict(my.glm, data.frame(x="b"), se.fit=T)
#visualize it and see the SE cross 0
plot(1:2,c(a.fit$fit,b.fit$fit), pch=19, ylim=c(-2,4))
segments(1:2, c(a.fit$fit-a.fit$se.fit, b.fit$fit-b.fit$se.fit),
1:2, c(a.fit$fit+a.fit$se.fit, b.fit$fit+b.fit$se.fit))
lines(0:3,rep(0,4), lty=2)
-Jarrett
----------------------------------------
Jarrett Byrnes
Population Biology Graduate Group, UC Davis
Bodega Marine Lab
707-875-1969
http://www-eve.ucdavis.edu/stachowicz/byrnes.shtml
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