search for: wiggliness

...Ms 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...

...ion 4.1.6 that it might be useful to shrink a single smooth by adding S=S+epsilon*I to the penalty matrix S. The context was the need to be able to shrink the term to zero if appropriate. I'd like to do this in order to shrink the coefficients towards zero (irrespective of the penalty for "wiggliness") - but not necessarily all the way to zero. IE, my informal prior is to keep the contribution of a specific term small. 1) Is adding eps*I to the penalty matrix an effective way to achieve this goal? 2) How do I accomplish this in practice using mgcv::gam? Thanks. -- View this message in...

Hi I'm trying to do a GAM analysis and have the following codes entered into R (density is = sample number, alive are the successes) density<-as.real(density) y<-cbind(alive,density-alive) library(mgcv) m1<-gam(y~s(density),binomial) at which point I get the following error message Error in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) : A term has fewer unique

...ering 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: >&g...

Dear R users, I'm new to both R and to this list and would like to get advice on how to build generalized additive models in R. Based on the description of gam, which I found on the R website, I specified the following model: model1<-gam(ST~s(MOWST1),family=binomial,data=strikes.S), in which ST is my binary response variable and MOWST1 is a categorical independent variable. I get the