I have made a logistic model for the probability a child will be immunized.
Here is the model:
M3 <- lmer(immun ~ kid2p + order23 + order46 + order7p + indNoSpa + indSpa +
momWork + rural + pcInd81 + (1|mom), family=binomial(link="logit"))

'mom' designates a child's family, where all the families have been indexed

I'm trying to predict the probability a new born child to a family will be
immunized.  Here's what I have:


new.info<- data.frame(kid2p=0, order23=0, order46=1, order7p=0, indNospa=0,
indSpa=0, momWork=1, rural=0, pcInd81=.0086511, mom=245)

pred.interval<-predict(M3, new.info, level=.95)


This function supposedly gives me the 95% confidence interval and
probability estimate.  I could just take the regression coefficients and
compute the probability manually, but I'm not sure how to deal with the
'mom' input varying by intercept.  Can I pull out the intercept value for
mom=245?  If so, I could just compute it all based on the regression summary
and a simulation.

-chris

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