It's ok to use an approximate F test here, provided you are careful with
the interpretation. e.g. do look at the differences in EDF and not just
the p-value (negative or very small EDF differences will render the
p-value essentially meaningless). however...
Where-ever possible it is usually better to test using `summary' or
`anova' with a single model argument. The approximations used by these
are better founded than the approximate F-test. i.e. take a look at the
p-values associated with the 's(y)' term in the larger model.
best,
Simon
On 20/09/13 13:37, Lucas Holland wrote:
Hey all,
I've fitted two GAMs to some data using mgcv. The only difference between the
two models is that one includes an additional smooth term (the smooth terms are
s(x), s(y) and s(log(y)), the difference being that one model contains s(y) as
additional term whereas the other one only contains s(x) and s(log(y)) - x and
y being my explanatory variables).
I'm now trying to decide between those two models. There's no difference in
deviance explained or R^2 and the diagnostic plots returned by gam.check() look
fairly similar although the one of the fuller model looks slightly more
satisfactory as far as the histogram of the residuals is concerned.
I'm wondering whether it is appropriate to conduct an approximate F test using
the anova function. I'm not 100% clear I've understood the documentation on
that completely. Is it appropriate to conduct such a test if the only
difference between models is the inclusion/exclusion of a smooth term?
Conducting the test, I get the result that there's no reason to reject the null
hypothesis that the simpler model (without s(y)) is correct.
Thanks!
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Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
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