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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