search for: glrt

Dear useRs, I am using mgcv version 1.7-16. When I create a model with a few non-linear terms and a random intercept for (in my case) country using s(Country,bs="re"), the representative line in my model (i.e. approximate significance of smooth terms) for the random intercept reads: edf Ref.df F p-value s(Country) 36.127 58.551 0.644

...ng variance components, of course, since the null hypothesis often involves restricting some variance parameters to the edge of their possible range, which rather messes up the Taylor expansion about the null parameter values that underpins the large sample distributional results used in the glrt. Your example does involve such a problematic comparison, but the result is so clear cut here that there is not really any doubt that inv_2 is better in this case (I wonder if inv_1 even passes basic model checking?). See Pinheiro and Bates (2000) for more info. hope this is some use, Simo...

We are using GAM in mgcv (Wood), relatively new users, and wonder if anyone can advise us on a problem we are encountering as we analyze many short time series datasets. For each dataset, we have four models, each with intercept, predictor x (trend), z (treatment), and int (interaction between x and z). Our models are Model 1: gama1.1 <- gam(y~x+z+int, family=quasipoisson) ##no smooths Model

Dear UseRs, I built varying coefficient models (in mgcv) for two groups separately, with one explanatory and one moderator variable (see the example below). # ------- #  Example: # ------ # generate moderator variable (can the same for both groups) modvar <- c(1:1000) # generate group1 values x1 <- rnorm(1000) y1 <- scale(cbind(1,poly(modvar,2))%*%c(1,2,1)*x1 + rnorm(1000,0,0.3)) #

Dear R-helpers, I'd like to understand how to test the statistical significance of a random effect in gamm. I am using gamm because I want to test a model with an AR(1) error structure, and it is my understanding neither gam nor gamm4 will do the latter. The data set includes nine short interrupted time series (single case designs in education, sometimes called N-of-1 trials in medicine)