Hi, folks,
x=1:10
y=rep(2:6,2)
lin=lm(y~x)
x=3:12
new=predict(lin,se.fit=T)
#se.fit: the standard error of the predicted means, namely, the square root
of Var( E[y|x] | x)
# How can I generate the variances of the new observations? Namely the
square root of var(y|x),
## Which I think should be much larger than the values from se.fit=T.
The reason why I need to know the estimations of expectation and also
variance is that Y=ln(Z) and I need to know the expectation of Z.
Thanks
Yi
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
