[R] ANOVA Table for Full Linear Model?

[Peter Dalgaard]

Jason R. Finley wrote:
> Hello,
> I have spent a good deal of time searching for an answer to this but  
> have come up empty-handed; I apologize if I missed something that is  
> common knowledge.
>
> I am trying to figure out how to get an ANOVA table that shows the  
> sum of squares. degrees of freedom, etc, for the full model versus  
> the error (aka residuals).
>
> Here is an example of the kind of table I'd like to get:
>
> Analysis of Variance
> Source       DF          SS          MS         F        P
> Regression    1      8654.7      8654.7    102.35    0.000
> Error        75      6342.1        84.6
> Total        76     14996.8
>
> This kind of table is prevalent throughout my statistics textbook,  
> and can apparently be easily obtained in other statistical software  
> tools.  I'm not saying this as a gripe, but just as evidence that I'm  
> not trying to do something obviously bizarre.
>
>
> Here is an example of the only kind of ANOVA table for a single  
> linear model that I've been able to get using R:
>
>  > regression9 <- lm(y ~ x1 + x2 + x3, data=data9)
>  > anova.lm(regression9)
> Analysis of Variance Table
>
> Response: y
>            Df Sum Sq Mean Sq F value    Pr(>F)
> x1         1 8275.4  8275.4 81.8026 2.059e-11 ***
> x2         1  480.9   480.9  4.7539   0.03489 *
> x3         1  364.2   364.2  3.5997   0.06468 .
> Residuals 42 4248.8   101.2
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
> Is there a way to get an ANOVA table with the full linear regression  
> model considered as a whole rather than broken down into each  
> additional predictor variable?  In other words, is there a way to get  
> the former kind of table?
>   
The logical thing to do would be (please avoid calling methods like 
anova.lm directly)

# following example(anova.lm):

> anova(update(fit,~1),fit)

Analysis of Variance Table

Model 1: sr ~ 1

Model 2: sr ~ pop15 + pop75 + dpi + ddpi

  Res.Df    RSS Df Sum of Sq      F    Pr(>F)    

1     49 983.63                                  

2     45 650.71  4    332.92 5.7557 0.0007904 ***


If you really want the column of MS, you have a little extra work to do. 
Notice also that the F test is part of the standard summary(fit)


> Again, apologies if I'm missing something basic.
> thanks very much,
> ~jason
>
>
> PS - I am on Mac OSX 10.4.8 using R 2.4.1 GUI 1.18 (4038)
>
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> Jason R. Finley
> Graduate Student, Department of Psychology
> Cognitive Division
> University of Illinois, Urbana-Champaign
>
> uniace at mac.com
> jrfinley at uiuc.edu
> http://www.jasonfinley.com/
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
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