[R] Doing partial-f test for stepwise regression

[zhuanyi at zay.name]

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?

Thanks in advance for help from anyone!

Regards,
Anyi Zhu