Dear all, I am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme".
Since the smoothing splines can be written as random effects, is it correct to use an (approximate) likelihood ratio test for this? If so, which is the correct number of degrees of freedom? Sometime the function LogLik() seems to provide strange results regarding the number of degrees of freedom (df) for the gam, for instance in the example I copied below the df for the "gamm" are equal to the ones for the "lme", but the summary(model.gam) seems to indicate a much higher edf for the gamm. I would be very grateful to anybody who could point out a solution, Best wishes, Carlo Example below: ---- rm(list = ls()) library(mgcv) library(nlme) set.seed(123) x <- runif(100,1,10) # regressor b0 <- rep(rnorm(10,mean=1,sd=2),each=10) # random intercept id <- rep(1:10, each=10) # identifier y <- b0 + x - 0.1 * x^3 + rnorm(100,0,1) # dependent variable f1 <- lme(y ~ x + I(x^2), random = list(id=~1) , method="ML" ) # lme model f2 <- gamm(y ~ s(x), random = list(id=~1), method="ML" ) # gamm ## same number of "df" according to logLik: logLik(f1) logLik(f2$lme) ## much higher edf according to summary: summary(f2$gam) ----------- ______________________________________________ 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.
