It seems that you want to do is in a Anova with the within factors
"factCond" and "factRoi", right?
If this is correct, then you should try:
summary(aov(vectdata~factCond*factRoi+Error(factCond*factRoi)))
Christophe Pallier
www.pallier.org
Bela Bauer wrote:
Hi,
I'm still trying to figure out that GLM procedure in SAS.
Let's start with the simple example:
PROC GLM;
MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21
col23 =/nouni;
repeated roi 6, ord 2/nom mean;
TITLE 'ABDERUS lat ACC 300-500';
That's the same setup that I had in my last email. I have three
factors: facSubj,facCond and facRoi. I had this pretty much figured
out with three lengthy calls to lm(), but I have to extend my code to
also work on models with four, five or even six factors, so that
doesn't seem like a practical method any more. I've tried with the
following code with glm(),anova() and drop1() (I use sum contrasts to
reproduce those Type III SS values); I've also tried many other
things, but this is the only somewhat reasonable result I get with glm.
> options(contrasts=c("contr.sum","contr.poly"))
> test.glm <- glm(vecData ~ (facCond+facSubj+facRoi)^2)
> anova(test.glm,test="F")
Analysis of Deviance Table
Model: gaussian, link: identity
Response: vecData
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 239 1429.30
facCond 1 2.21 238 1427.09 3.0764 0.08266 .
facSubj 19 685.94 219 741.16 50.2463 < 2.2e-16 ***
facRoi 5 258.77 214 482.38 72.0316 < 2.2e-16 ***
facCond:facSubj 19 172.70 195 309.68 12.6510 < 2.2e-16 ***
facCond:facRoi 5 10.37 190 299.31 2.8867 0.01803 *
facSubj:facRoi 95 231.05 95 68.26 3.3850 4.266e-09 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> drop1(test.glm,scope=.~.,test="F")
Single term deletions
Model:
vecData ~ (facCond + facSubj + facRoi)^2
Df Deviance AIC F value Pr(F)
<none> 68.26 671.33
facCond 1 70.47 676.97 3.0764 0.08266 .
facSubj 19 754.19 1209.89 50.2463 < 2.2e-16 ***
facRoi 5 327.03 1037.35 72.0316 < 2.2e-16 ***
facCond:facSubj 19 240.96 936.05 12.6510 < 2.2e-16 ***
facCond:facRoi 5 78.63 695.27 2.8867 0.01803 *
facSubj:facRoi 95 299.31 836.09 3.3850 4.266e-09 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Now, unfortunately this just isn't the output of SAS (roi corresponds
to facRoi, ord corresponds to facCond)
Source DF Type III SS Mean Square F Value Pr
> F
roi 5 258.7726806 51.7545361 21.28
<.0001
Error(roi) 95 231.0511739 2.4321176
Adj Pr > F
Source G - G H - F
roi <.0001 <.0001
Error(roi)
Greenhouse-Geisser Epsilon 0.5367
Huynh-Feldt Epsilon 0.6333
Source DF Type III SS Mean Square F Value
Pr > F
ord 1 2.2104107 2.2104107 0.24
0.6276
Error(ord) 19 172.7047994 9.0897263
Source DF Type III SS Mean Square F Value
Pr > F
roi*ord 5 10.37034918 2.07406984 2.89
0.0180
Error(roi*ord) 95 68.25732255 0.71849813
Adj Pr > F
Source G - G H - F
roi*ord 0.0663 0.0591
Error(roi*ord)
Greenhouse-Geisser Epsilon 0.4116
Huynh-Feldt Epsilon 0.4623
As you can see, I get a correct p and F values for the facCond:facRoi
interaction, but everything else doesn't come out right. The drop1()
call gives me the correct degrees of freedom, but residual degrees of
freedom seem to be wrong.
Could you give me any hints how I could get to the SAS results? For
the lm() calls that work (in special cases), refer to my posting from
last Friday.
I also have a 4-factorial example, and I've been told that people
around here do 5- or 6-factorial multivariant ANOVAs, too, so I need a
general solution.
Thanks a lot!
Bela
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