Hi, If I understand correctly the
Var(X \hat{\beta}) = X (X'X)^{-1}X' \sigma^2, where X will now be "x.pred". Which should be easily obtained by performing the matrix computation and multiplying it with the estimate of the variance. For more details about different aspects of the estimate and variance of the predictor refer to page 39 of http://www.stat.lsa.umich.edu/~faraway/book/ Ritwik Sinha On 8/23/06, Arnab mukherji <[EMAIL PROTECTED]> wrote: > > Hi ! > > I am trying to get at the covariance of the predictions of a linear model. > Suppose the we have: > > > x<-runif(1000) > > y<-2 + 25x*x +rnorm(1000) > > lm1 <-lm(y~x, data = data.frame(y = y, x=x)) > > x.pred <-runif(10) > > y.hat <- predict(lm1, newdata = data.frame(x=x.pred)) > > I was wondering how to get an estimate of the covariance of y.hat which > would be a 10 x 10 matrix telling be the uncertainty in each of the > predictions. > > thanks > > Arnab > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > -- Ritwik Sinha Graduate Student Epidemiology and Biostatistics Case Western Reserve University http://darwin.cwru.edu/~rsinha [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch 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.