How should the weights be treated? If they are multiple observation weights (a weight of
"3" is shorthand for 3 subjects) that leads to a different likelihood than sampling
weights ("3" means to give this one subject more influence). The clogit command can't
read your mind and so has chosen not to make a guess.
Also, please do not post in html. As you see below it leads to a mangled
message.
Terry Therneau
On 12/22/2015 05:00 AM, r-help-requ...@r-project.org wrote:
Merry Christmas everyone:
I have the following data(mydat) and would like to fit a conditional logistic regression
model considering "weights".
id? case?exposure?? weights
1?????1?????????1????????? 2
1?????0?????????0????????? 2
2?????1?????????1????????? 2
2?????0?????????0????????? 2
3?????1?????????1????????? 1
3?????0?????????0????????? 1
4?????1?????????0???????? ?2
4?????0?????????1????????? 2 ?The R function"clogit" is for such purposes but it ignores
weights.?I tried function"mclogit" instead which seems that it considers the weights
option:##############################################################options(scipen=999)library(mclogit)#
create the above data frameid????????? = c(1,1,2,2,3,3,4,4)case????? =?c(1,0,1,0,1,0,1,0)exposure
= c(1,0,1,0,1,0,0,1)weights? = c(2,2,2,2,1,1,2,2)(mydata??= data.frame(id,case,exposure,weights))
fit??????= mclogit(cbind(case,id) ~ exposure,weights=weights,
data=mydata)summary(fit)######################################################################
The answer,however,?doesn't seem to be?correct. Could anyone?pleaseprovides me
with some solution to this??Thanks in advance,Keramat Nourijelyani,PhD??
[[alternative HTML version deleted]]
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