On Thu, 27 May 2004 16:34:58 +0930 "David J. Netherway" <[EMAIL PROTECTED]> 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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html