I verified the result from the following with output from JMP 6 on the
same data (don't have SAS: don't need it):
set.seed(631)
n <- 100
dat <- data.frame(y=rnorm(n), A=factor(sample(1:2, n, replace=TRUE)),
B=factor(sample(1:2, n, replace=TRUE)),
C=factor(sample(1:2, n, replace=TRUE)),
d=rnorm(n))
fm <- lm(y ~ A + B + C + d, dat)
## Form a data frame of points to predict: all combinations of the
## three factors and the mean of the covariate.
p <- data.frame(expand.grid(A=1:2, B=1:2, C=1:2))
p[] <- lapply(p, factor)
p <- cbind(p, d=mean(dat$d))
p <- cbind(yhat=predict(fm, p), p)
## lsmeans for the three factors:
with(p, tapply(yhat, A, mean))
with(p, tapply(yhat, B, mean))
with(p, tapply(yhat, C, mean))
Andy
From: Xingwang Ye
>
> Dear all,
>
> I search the mail list about this topic and learn that no
> simple way is available to get "lsmeans" in R as in SAS.
> Dr.John Fox and Dr.Frank E Harrell have given very useful
> information about "lsmeans" topic.
> Dr. Frank E Harrell suggests not to think about lsmeans,
> just to think about what predicted values wanted
> and to use the predict function. However, after reading
> the R help file for a whole day, I am still unclear how to do it.
> Could some one give me a hand?
>
> for example:
>
> A,B and C are binomial variables(factors); d is a continuous
> variable ; The response variable Y is a continuous variable too.
>
> To get lsmeans of Y according to A,B and C, respectively, in
> SAS, I tried proc glm data=a; class A B C; model Y=A B C d;
> lsmeans A B C/cl; run;
>
> In R, I tried this:
> library(Design)
> ddist<-datadist(a)
> options(datadist="ddist")
> f<-ols(Y~A+B+C+D,data=a,x=TRUE,y=TRUE,se.fit=TRUE)
>
> then how to get the "lsmeans" for A, B, and C, respectively
> with predict function?
>
>
>
> Best wishes
> yours, sincerely
> Xingwang Ye
> PhD candidate
> Research Group of Nutrition Related Cancers and Other Chronic
> Diseases
> Institute for Nutritional Sciences,
> Shanghai Institutes of Biological Sciences,
> Chinese Academy of Sciences
> P.O.Box 32
> 294 Taiyuan Road
> Shanghai 200031
> P.R.CHINA
>
>
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