Dear colleagues, I had a question wrt the car package. How do I evaluate whether a simpler multivariate regression model is adequate?
For instance, I do the following: ami <- read.table(file = "http://www.public.iastate.edu/~maitra/stat501/datasets/amitriptyline.dat";, col.names=c("TCAD", "drug", "gender", "antidepressant","PR", "dBP", "QRS")) ami$gender <- as.factor(ami$gender) ami$TCAD <- ami$TCAD/1000 ami$drug <- ami$drug/1000 library(car) fit.lm <- lm(cbind(TCAD, drug) ~ gender + antidepressant + PR + dBP + QRS, data = ami) fit.manova <- Manova(fit.lm) fit1.lm <- update(fit.lm, .~ . - PR - dBP - QRS) fit1.manova <- Manova(fit1.lm) Is there an easy way to find out whether the reduced model is adequate? I am thinking of something similar to the anova() function, I guess? Many thanks and best wishes, Ranjan ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
