[R] Getting the same values of adjusted mean and standard errors as SAS

[David J. Netherway]

Hello,
 
I am trying to get the same values for the adjusted means and standard 
errors using R that are given in SAS for the
following data. The model is Measurement ~ Age + Gender + Group. I can 
get the adusted means at the mean age  
by using predict. I do not know how to get the appropriate standard 
errors at the adjusted means for Gender
using values from predict. So I attempted to get them directly from the 
residuals as follows. The data is at the end
of the email. While there is a match for the males there is a large 
difference for the females indicating that what I am doing is wrong.
 
#  
meanAge <- mean(dd$Age)
meanAgeM <- mean(dd$Age[d$Gender=="M"])
meanAgeF <- mean(dd$Age[d$Gender=="F"])
 
# determine adjusted means for the males and females at meanAge using 
predict
# set up data frame to get predicted values at meanAge
evalDF <- data.frame(Age = meanAge, Gender = c("F","M"),  
       Group = c("1NC","1NC", "2UCLP", "2UCLP", "3BCLP", "3BCLP", 
"4ICP", "4ICP", "5CLPP", "5CLPP"))
mod <-lm(Measurement ~ Age + Gender + Group, data=dd)
pred <- predict(mod, evalDF, se.fit = TRUE)
adjDF <- data.frame(evalDF,fit = pred$fit, se = pred$se.fit)
adjMeanMale <- mean(adjDF$fit[adjDF$Gender=="M"]); # match: 3.889965 cf 
3.88996483
adjMeanFemale <- mean(adjDF$fit[adjDF$Gender=="F"]); # match: 3.91111 cf 
3.91111036
 
# Try to get standard errors at the adjusted means for Gender as follows:
ddr <- data.frame(dd, res = residuals(mod))
nM <- summary(ddr$Gender)[["M"]]
seRegM <- sqrt(mean( ddr$res[ddr$Gender=="M"]**2 ))
sxxM <- sum((dd$Age[d$Gender=="M"]-meanAgeM)**2)
syM <- seRegM * sqrt(1/nM + (meanAge-meanAgeM)**2/sxxM); #0.1103335 cf 
0.11032602 - matches to 5 decimal places
nF <- summary(ddr$Gender)[["F"]]
seRegF <- sqrt(mean( ddr$res[ddr$Gender=="F"]**2 ))
sxxF <- sum((dd$Age[d$Gender=="F"]-meanAgeF)**2)
syF <- seRegF * sqrt(1/nF + (meanAge-meanAgeF)**2/sxxF); # wrong: 
0.07279221 cf 0.14256466
 
 
 > dd
   Measurement Age Gender Group
1       3.8  94      M 3BCLP
2       2.7  88      F 3BCLP
3       3.0 155      M 3BCLP
4       2.7  33      M 3BCLP
5       4.6 109      M 5CLPP
6       5.1 325      M 5CLPP
7       3.9  79      M 5CLPP
8       4.2 126      M 5CLPP
9       3.9  77      F 5CLPP
10      4.0  61      F 5CLPP
11      3.6  49      F 5CLPP
12      3.7  14      F  4ICP
13      4.2 160      F  4ICP
14      3.9  60      M  4ICP
15      5.0  61      M  4ICP
16      3.9 222      F  4ICP
17      3.8  82      F  4ICP
18      4.8 340      F  4ICP
19      3.2 206      M  4ICP
20      3.8  19      M   1NC
21      4.9 166      M   1NC
22      3.8  93      M   1NC
23      3.6 142      M   1NC
24      4.8 241      M   1NC
25      3.9  81      M   1NC
26      4.5  41      M   1NC
27      5.1 244      F   1NC
28      4.6 100      M   1NC
29      5.1 122      F   1NC
30      4.7 194      F   1NC
31      5.1 297      M   1NC
32      3.9  69      M 2UCLP
33      2.5 141      M 2UCLP
34      3.2 104      M 2UCLP
35      3.8  90      M 2UCLP
36      3.8  92      M 2UCLP
37      3.6 149      F 2UCLP
38      3.8  53      F 2UCLP
39      4.7 111      M 2UCLP
40      3.8 116      F 2UCLP
41      3.3  81      M 2UCLP
 >