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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