# [R] Doing partial-f test for stepwise regression

Michael Kubovy kubovy at virginia.edu
Sun Apr 1 10:46:26 CEST 2007

```On Apr 1, 2007, at 1:54 AM, zhuanyi at zay.name wrote:

> Hello all,
> I am trying to figure out an optimal linear model by using stepwise
> regression which requires partial f-test, I did some Googling on the
> Internet and realised that someone seemed to ask the question before:
>
> Jim Milks <jrclmilks at joimail.com> writes:
>> Dear all:
>>
>> I have a regression model that has collinearity problems (between
>> three regressor variables). I need a F-test that will allow me to
>> compare between full (with all variables) and partial models (minus
>> 1=< variables). The general F-test formula I'm using is:
>>
>> F = {[SS(full model) - SS(reduced model)] / (#variables taken out)} /
>> MSS(full model)
>>
>> Unfortunately, the ANOVA table parses the SS and MSS between the
>> variables and does not give the statistics for the regression
>> model as
>> a whole, otherwise I'd do this by hand.
>>
>> So, really, I have two questions: 1) Can I just add up all the SS and
>> MSS for all the variables to get the model SS and MSS and 2) Are
>> there any functions or packages I can use to calculate the F-
>> statistic?
>> Just use anova(model1, model2).
>> (One potential catch: Make sure that both models are fitted to the
>> same
>> data set. Missing values in predictors may interfere.)
>
> However, in the answer provided by Mr. Peter Dalgaard,(use
> anova(model1,model2) I could not understand what model1 and model2 are
> supposed to referring to, which one is supposedly to be the full
> model and
> which one is to be the partial model? Or it does not matter?

You can tell which is which by looking at the degrees of freedom.

_____________________________
Professor Michael Kubovy
University of Virginia
Department of Psychology
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