Try something like this:
fits <- list(500)
for (i in 1:500)
{
if (sum(table1$id == i) == 0) fits[[i]] <- NA
else fits[[i]] <- lm(y~t,data=table1,subset=(id==i))
}
--- On Wed, 4/6/08, Manli Yan <[EMAIL PROTECTED]> wrote:
> From: Manli Yan <[EMAIL PROTECTED]>
> Subject: [R] linear model in the repeated data type~
> To: r-help@r-project.org
> Received: Wednesday, 4 June, 2008, 2:06 PM
> here is the data:
> y<-c(5,2,3,7,9,0,1,4,5)
> id<-c(1,1,6,6,7,8,15,15,19)
> t<-c(50,56,50,56,50,50,50,60,50)
> table1<-data.frame(y,id,t)//longitudinal data
>
> what I want to do is to use the linear model for each id
> ,then get the
> estimate value,like:
>
> fit1<-lm(y~t,data=table1,subset=(id==1))
>
> but ,you can see the variable "id" is quite
> irregular,they are not arranaged
> in order and many number missing,if I write a loop by using
> "for",it will
> give me a lot "NA",
> and for sure ,I dont want to type id=## for about 500
> times,any one know how
> to deal with it?
>
> [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained,
> reproducible code.
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