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Given a design matrix, as generated by the design.des function, the design.dat function appends (1) fitted values on the response scale ("fitted"), (2) the delta method standard error for the fitted value ("se"), (3) the lower limit of a confidence interval around the fitted value ("ll"), and the upper limit of a confidence interval around the fitted value ("ul").
For example:
library(catregs) data("Mize19AH") m1 <- glm(alcB ~woman*parrole + age + race2 + race3 + race4 + income + ed1 + ed2 + ed3 + ed4,family="binomial",data=Mize19AH) des2<-margins.des(m1,expand.grid(woman=c(0,1),parrole=c(0,1))) margins.dat(m1,des2,rounded=5) des1 <- margins.des(m1,expand.grid(parrole=1,woman=1)) margins.dat(m1,des1,rounded=5) des3 <- margins.des(m1,expand.grid(age=seq(20,75,5),parrole=c(0,1))) a<- margins.dat(m1,des3,rounded=5) a
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