I'm sure this is an easy thing to do, but I can't seem to get it right. My
first question is: Is there a way to set the intercept of a glm to be an
overall average of the response variable, as opposed to the coefficient
for a particular term.
For example, let's say there are 2 variables (1 and 2) with levels A, B, C
and D, E, F, respectively. The default glm output provides an intercept
term that is the coefficient for A*D and seperate terms for B, C, F, B*F,
and C*F. My issue is that I don't always have estimates for certain
interactions. When I try to predict values for new data using the
coefficients from the model, predict.glm assumes the NAs are 0 and,
therefore, those coefficients have the same estimate as the intercept plus
other main effects for which there are data. So, if there's an easy way
to do that, that would be great. If not...
...I could do it by changing the design matrix. I've played with glm.fit
and model.matrix to create the design matrices, but I'm having trouble
using them in glm. I created a matrix and populated it, but when I use
the saved glm.fit as the "method" argument in glm, I get:
'Error in switch(method, model.frame = return(mf), glm.fit = 1,
stop("invalid 'method': ", :switch: EXPR must return a length 1 vector"
EX
glm.fit(x=model.matrix(Y ~ X+Z), y=c(1,...,1))
glm(Y ~ X+Z, family='binomial', method=glm.fit)
So, I guess my question is: How do I include the design matrix so that the
glm function uses it instead of the default design matrix?
Thanks,
Adam Green
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