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

[David J. Netherway]

Thanks for the help.

Both the "Design" package and the "effects" package look as though they are
what I need although it will probably take me a while to get on top of both.

I have had a brief  go at the Design package and the contrast function 
is particularly useful.

A question on the Design package:

There are 5 types for factor "group", one is the reference - call it "a".

f <- ols(y ~ age + sex + group, data=dd)
contrast(f, list(group='a'), list(group='b'))

I can use this to contrast pairs but can I use this to contrast b against c,d, and e as a group.
Also "a" against the rest?


Thanks, David

Frank E Harrell Jr wrote:

>On Thu, 27 May 2004 16:34:58 +0930
>"David J. Netherway" <david.netherway at adelaide.edu.au> wrote:
>
>  
>
>>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$Gender=="F"])
>>    
>>
>. . . .
>
>By using sex-specific means of age you are not getting adjusted estimates
>in the usual sense.
>
>I prefer to think of effects as differences in predicted values rather
>than as complex SAS-like contrasts. The Design package's contrast function
>makes this easy (including SEs and confidence limits):
>
>library(Design)   # also requires Hmisc
>d <- datadist(dd); options(datadist='d')
>f <- ols(y ~ age + sex + group, data=dd)
>contrast(f, list(sex='M'), list(sex='F'))   # usual adjusted difference M
>vs F
>contrast(f, list(sex='M',age=mean(dd$age[dd$sex=='M']),
>            list(sex='F',age=mean(dd$age[dd$sex=='F')) # M vs F not
>holding age constant
>
>You can also experiment with specifying age=tapply(age, sex, mean,
>na.rm=TRUE) using some of the contrast.Design options.
>---
>Frank E Harrell Jr   Professor and Chair           School of Medicine
>                     Department of Biostatistics   Vanderbilt University
>  
>