I have done a logistic glm using Age as explanatory variable for some allergic event.
#the model
model2d<-glm(formula=AEorSAEInfecBac~Age,family=binomial("logit"),data=emrisk)
#predictions for age 30 and 60
preds<-predict(model2d,data.frame(Age=c(30,60)),se.fit=TRUE)
# prediction interval
predsxx<-cbind(fit=preds$fit,lower=preds$fit-1.96*preds$se,upper=preds$fit+1.96*preds$se)
#transformation
model2dres<-family(model2d)$linkinv(predsxx)In my next step I want to know the confidence interval (CI) for the change in risk for the allergic event to occur for age 60 compared to age 30.
The estimates from the model suggest a 80 pct. higher risk for age 60 compared to age 30.
(100*model2dres[2]/model2dres[1])
But how should I get the 95% CI of the 80pct. increase??
I've looked in the effects package but did'nt find an answer.
Any hints?
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