
Dear R users,
I want to draw standard error lines for the predicted regression line
estimated by logistic regression using lmer. I have two predictors: cafr and
its quadratic form I(cafr^2), where cafr is a variable centered around the
mean of original variable. Now, the estimated value from the fitted model
will be,
(model@X)%*%fixef(model)
In the logit scale, the mean sum of square from fitted model will be, 
sesample=sqrt(sum(resid(model)^2)/(n-p-1)), where p is the degrees of
freedom used for fitting.
Could someone make a judgement if it is reasonable to calculate standard
error of the estimated value by
sesample*sqrt(vector%*%ginv(t(model@X)%*%model@X)%*%t(vector))
, where vector is the (1,cafr,I(cafr^2)) which representing empirical data
vector at considered point.
If this is correct, I think I can use this method to draw standard error
line. Otherwise, would you please suggest a reasonable one?
Thank you very much for your attention!
Yours sincerely, Jianghua
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