From: Liaw, Andy <andy_liaw_at_merck.com>

Date: Sat 29 Oct 2005 - 03:25:18 EST

*> anova(lm(y ~ x2 + x1, dat))
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Analysis of Variance Table

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 Received on Sat Oct 29 03:33:52 2005

Date: Sat 29 Oct 2005 - 03:25:18 EST

anova.lm() gives the sequential tests:

*> set.seed(1)
**> dat <- data.frame(y=rnorm(10), x1=runif(10), x2=runif(10))
**> anova(lm(y ~ x1 + x2, dat))
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Analysis of Variance Table

Response: y

Df Sum Sq Mean Sq F value Pr(>F) x1 1 1.1483 1.1483 2.0943 0.1911 x2 1 0.4972 0.4972 0.9068 0.3727 Residuals 7 3.8383 0.5483

Analysis of Variance Table

Response: y

Df Sum Sq Mean Sq F value Pr(>F) x2 1 0.5165 0.5165 0.9419 0.3641 x1 1 1.1291 1.1291 2.0592 0.1944 Residuals 7 3.8383 0.5483

The SS, F-stat, etc. would be invariant to order only if the terms are orthogonal.

Andy

*> From: Jarrett Byrnes
**>
*

> I'm curious, I realize there are methods for Type II and III sums of

*> squares, and yet, when I've been constructing models with lm, I've
**> noticed that position of the term of the model has not mattered in
**> terms of its p-value. Does lm use sequential Type I sums of squares,
**> or something else?
**>
**> Thanks!
**>
**> -Jarrett
**>
**> ______________________________________________
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**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide!
**> http://www.R-project.org/posting-guide.html
**>
**>
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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 Received on Sat Oct 29 03:33:52 2005

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