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

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From: Frank E Harrell Jr (f.harrell@vanderbilt.edu)
Date: Mon 31 May 2004 - 14:38:44 EST


Message-id: <40BAB6D4.1050205@vanderbilt.edu>

David J. Netherway wrote:
> 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?
>

Type ?contrast.Design. You'll see examples of 'vector' contrasts with
and without weighted/unweighted averaging of effects. E.g. contrast(f,
list(group='b'), list(group=c('c','d','e'))) will give 3 contrasts.
There is an option to average these.

Frank
>
> Thanks, David
>
> Frank E Harrell Jr wrote:
>
>> On Thu, 27 May 2004 16:34:58 +0930
>> "David J. Netherway" <david.netherway@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
>>
>>
>
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>

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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