p-level in packages mgcv and gam
But what about another analogy, that of polynomials? You may not be sure what degree polynomial to use, and you have not decided before analysing your data. You fit different polynomials to your data, checking if added degrees increase r2 sufficiently by doing F-tests. I thought it was the same thing with GAMs. You can fit a model with 4 df, and in some cases it is of interest to see if this is a better fit than a linear fit. But why can't you also check if 7df is better than 4df? And if you used mgcv first and it tells you that 7df is better than 4df, why bother repeating the comparison 7df against 4df, why not just take the p-value for the model with 7df (fixed)? Denis Maybe one is in Le 05-09-28 ?? 12:04, Peter Dalgaard a ??crit :
Thomas Lumley <tlumley at u.washington.edu> writes:
Bob, on the other hand, chooses the amount of smoothing depending on the data. When a 4 df smooth fits best he ends up with the same model as Alice and the same p-value. When some other df fits best he ends up with a different model and a *smaller* p-value than Alice.
This doesn't actually follow, unless the p-value (directly or indirectly) found its way into the definition of "best fit". It does show the danger, though. -- O__ ---- Peter Dalgaard ??ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907