Dear Ben, or anybody else, of course,
I'd be grateful if you could point me to a reference (different from ch. 4
"Linear models" in "Statistical Models in S" (Chambers & Hastie (1992)))
regarding the (asserted F-)distributional properties of the test statistic
(used, e.g., by anova.lm()) to compare model 1 with model 2 using the MSE
of model 3 in a sequence of three nested (linear) models? (A short
RSiteSearch() and a google search didn't lead me far ...)
Thx in advance!
Best regards -- Gerrit
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Dr. Gerrit Eichner Mathematical Institute, Room 212
gerrit.eich...@math.uni-giessen.de Justus-Liebig-University Giessen
Tel: +49-(0)641-99-32104 Arndtstr. 2, 35392 Giessen, Germany
Fax: +49-(0)641-99-32109 http://www.uni-giessen.de/cms/eichner
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On Wed, 21 Mar 2012, Ben Bolker wrote:
msteane <michellesteane <at> hotmail.com> writes:
I am using anova.lm to compare 3 linear models. Model 1 has 1 variable,
model 2 has 2 variables and model 3 has 3 variables. All models are fitted
to the same data set.
(I assume these are nested models, otherwise the analysis doesn't
make sense ...)
anova.lm(model1,model2) gives me:
Res.Df RSS Df Sum of Sq F Pr(>F)
1 135 245.38
2 134 184.36 1 61.022 44.354 6.467e-10 ***
anova.lm(model1,model2,model3) gives me:
Res.Df RSS Df Sum of Sq F Pr(>F)
1 135 245.38
2 134 184.36 1 61.022 50.182 7.355e-11 ***
3 133 161.73 1 22.628 18.609 3.105e-05 ***
Why aren't the 2nd row F values from each of the anova tables the same??? I
thought in each case the 2nd row is comparing model 2 to model 1?
From ?anova.lm:
Normally the F statistic is most appropriate, which compares the mean
square for a row to the residual sum of squares for the largest model
considered.
I figured out that for anova.lm(model1,model2)
F(row2)=Sum of Sq(row2)/MSE of Model 2
and for anova.lm(model1,model2,model3)
F(row2)=Sum of Sq(row 2)/MSE of Model 3 <-- I don't get why the MSE of
model 3 is being included if we're comparing Model 2 to Model 2
See above ...
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