On Mon, 20 Feb 2006, ronggui wrote:
I think nlme is not for logistic mixed effect model.
you should use glmmPQL in MASS or lmer in Matrix
There are two senses of 'logistic mixed effect model'. One is for a
continuous response as given by SSlogis, and nlme is appropriate. The
other is a glm with a binomial (or binary) response, and nlme is
inappropriate.
The issue here is that when the fixed includes an rhs, there are
parameters for intercept and slope for each level of SKILLS, although they
will be coded using contrasts. So 2 starting values are not enough.
2006/2/20, Mingyu Feng <[EMAIL PROTECTED]>:
Hello there,
I am using nlme() to fit a logistic mixed effect model on our data.
The outcome variable is binary.
I got the error when I wanted to add a group factor to my model.
My initial model is as below:
model.a <- nlme(response~ 1/(1 + exp( -intercept- u0 - slope*TIME -
u1*TIME)),
+ fixed=intercept+slope~1, random= u0+u1~1
|studentID,
+ start=c(slope=.01, intercept=-1), data=log.data,
method='ML')
This works fine on my data. But when i update it by adding a group factor
SKILLS,
I got the error message:
"Error in nlme.formula(response ~ 1/(1 + exp( -intercept- u0 - slope*TIME
:
starting values for the fixed component are not the correct length"
The model is as below:
model.a <- nlme(response~ 1/(1 + exp( -intercept- u0 - slope*TIME -
u1*TIME)),
+ fixed=intercept+slope ~ SKILLS, random= u0+u1~1
|studentID,
+ start=c(slope=.01, intercept=-1), data=log.data,
method='ML')
Does anybody see anything wrong with the "start" part of this model?
Thanks a lot!!
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