Hello,
I have encountered results which I am not sure how to interpret when using
anova.gam to compare 2 different models. For certain tests the results do not
include an F- or associated p-statistic. This happens when comparing certain
models and not others, and I do not discern a patten explaining when the test
works and when it does not.
Here is some output for some of my tests (y#, x1, and x2 are each 1-variable
vectors, while X is a matrix of several variables).
These results compare a model additively separable in x1 and x2 with a model in
which they are not assumed additively separable:
Model 1: y1 ~ s(x1) + s(x2) + X
Model 2: y1 ~ X + s(x1, x2)
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 3815.5111 29860.6
2 3810.3577 29898.8 5.1534 -38.2
No F statistic is computed, though the statistic is computed when other
dependent variables are used.
Here are some results for a similar analysis with a different dependent
variable:
Model 1: y2 ~ x1 + x2 + X
Model 2: y2 ~ s(x1) + s(x2) + X
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 3822.000 33970
2 3819.535 33921 2.465 49 2.2257 0.09578 .
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
> anova(EqQuad,EqGamAS,test="F")
Analysis of Deviance Table
Model 1: y2 ~ x1 + x1sq + x2 + x2sq + x1x2 + X
Model 2: y2 ~ s(x1) + s(x2) + X
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 3819.00000 33955
2 3819.53502 33921 -0.53502 34
Model 1: y2 ~ s(x1) + s(x2) + X
Model 2: y2 ~ X + s(x1, x2)
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 3819.5350 33921
2 3821.8323 33967 -2.2973 -46 2.2391 0.09863 .
An F statistic is reported for comparing the linear model with the additively
separable semiparametric model, and for comparing the additively separable model
with the non-additvely separable model, but not when comparing the partially
quadratic model (x#sq means x#^2) with the additively separable semiparametric
model.
I'm happy to provide more information about my dataset or my estimation, but
I don't know what might be helpful, as I really don't understand at all
the cause of this problem. My dataset is not small (about 3800 observations).
I will say that x2 has many observations of value 0.
I appreciate any light anyone can shed on this issue. Thank you very much.
Michael Milligan
Ph.D. Candidate
University of New Mexico
