Hello fellow R's, I do apologize if this is a basic question. I'm doing some GAMs using the mgcv package, and I am wondering what is the most appropriate way to determine how much of the variability in the dependent variable is explained by each term in the model. The information provided by summary.gam() relates to the significance of each term (F, p-value) and to the "wiggliness" of the fitted smooth (edf), but (as far as I understand) there is no information on the proportion of variance explained.
One alternative may be to fit alternative models without each term, and calculate the reduction in deviance. For example: m1=gam(y~s(x1) + s(x2)) # Full model m2=gam(y~s(x2)) m3=gam(y~s(x1)) ddev1=deviance(m1)-deviance(m2) ddev2=deviance(m1)-deviance(m3) Here, ddev1 would measure the relative proportion of the variability in y explained by x1, and ddev2 would do the same for x2. Does this sound like an appropriate approach? Julian Julian Burgos FAR lab University of Washington ______________________________________________ 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.
