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?
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
[EMAIL PROTECTED]
[EMAIL PROTECTED]
http://www.jasonfinley.com/
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
______________________________________________
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
