p-level in packages mgcv and gam
On Mon, 26 Sep 2005, Denis Chabot wrote:
But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result in a value close to the P=0.05 threshold, but I have one case with P=0.033. I thought, probably naively, that running a second model with fixed df, using the value of df found in the first model. I could not achieve this with mgcv: its gam function does not seem to accept fractional values of df (in my case 8.377).
No, this won't work. The problem is the usual one with model selection: the p-value is calculated as if the df had been fixed, when really it was estimated. It is likely to be quite hard to get an honest p-value out of something that does adaptive smoothing. -thomas