From: Denis Chabot
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
the data. When a 4 df smooth fits best he ends up with the
as Alice and the same p-value. When some other df fits
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.
--
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