Daniel Malter umd.edu> writes:
>
> id<-rep(c(1:100),each=2)
> obs<-rep(c(0:1),100)
> d<-rep(sample(c(-1,1),100,replace=T),each=2)
> base.happy<-rep(rnorm(100),each=2)
> happy<-base.happy+1.5*d*obs+rnorm(200)
>
> data<-data.frame(id,obs,d,happy)
>
> > I am statistically confused tonight. When
Daniel:
Your question should be addressed to R-sig-mixed-models, as it really
does not belong on r-help.
-- Bert
On Mon, Aug 22, 2011 at 5:11 PM, Daniel Malter wrote:
> Small bugs in my simulated data (corrected code below). However, that does
> not affect my question:
>
> id<-rep(c(1:100),eac
Small bugs in my simulated data (corrected code below). However, that does
not affect my question:
id<-rep(c(1:100),each=2)
obs<-rep(c(0:1),100)
d<-rep(sample(c(-1,1),100,replace=T),each=2)
base.happy<-rep(rnorm(100),each=2)
happy<-base.happy+1.5*d*obs+rnorm(200)
data<-data.frame(id,obs,d,happy)
Hi all,
I am statistically confused tonight. When the assumptions to a random
effects estimator are warranted, random effects should be the more efficient
estimator than the fixed effects estimator because it uses fewer degrees of
freedom (estimating just the variance parameter of the normal rathe
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