Ana, It's not really clear what you are trying to do. From your description, you aren't estimating anything. The code
a <- c(1, 0.6, 0.8) x <- rnorm(2) y <- crossprod(c(1, x), a) generates 'y'; but nothing is estimated. 'y' is going to have the same variance regardless of how many observations you generate, since the variance is coming from x1 and x2, not 'a'. Your question sounds a little like a homework problem. If it's not, please explain what you're trying to do. --Gray 2010/7/27 Ana De Barros <belindadebar...@gmail.com>: > No, but thanks anyway... > > > On 27/07/10 16:38, "Gray Calhoun" <gray.calh...@gmail.com> wrote: > >> Hi Ana, >> Does the "predict" function do what you want? Type in ?predict.lm >> --Gray >> >> On 7/27/10, Ana De Barros <belindadebar...@gmail.com> wrote: >>> Hi, >>> >>> Is there any way to estimate a DEPENDENT variable through a GLM/LM model? >>> >>> Suppose I have the linear model: y=a0+a1*x1+a2*x2 (a0=1, a1=0.6, a2=0.8, >>> x1~N(1,1), x2~N(0,1)). >>> The alphas and the auxiliary variables are given and I have to estimate y. >>> The point is if I estimate it, let零 say algebraically, I get high variances >>> that do not decrease as sample sizes increases... Is the any other way to do >>> this?... It is not compulsory to use these alphas but my Y is unknown... >>> >>> Any ideas?... >>> Thanks, >>> Ana >>> >>> [[alternative HTML version deleted]] >>> >>> >> > > > -- Gray Calhoun Assistant Professor of Economics, Iowa State University http://www.econ.iastate.edu/~gcalhoun/ ______________________________________________ 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.
