Hello, If you just want the mean and variance of log(y) try:
mean(log(y)) var(log(y)) if there is missing data, you can add na.rm=TRUE to both of those. If you want the mean and variance of the predicted ys mean(predict(linmod)) var(predict(linmod)) see ?mean ?var ?predict.lm #the specific method being used for predict() with model objects of class lm HTH, Josh On Mon, Jun 21, 2010 at 11:24 AM, Yi <liuyi.fe...@gmail.com> wrote: > Hi, folks, > > As seen in the following codes: > > x1=rlnorm(10) > x2=rlnorm(10,mean=2) > y=rlnorm(10,mean=10)### Fake dataset > linmod=lm(log(y)~log(x1)+log(x2)) > > After the regression, I would like to know the mean of y. Since log(y) is > normal and y is lognormal, I need to know the mean and variance of log(y) > first. I tried mean (y) and mean(linmod), but either one is what I want. > > Any tips? > > Thanks in advance! > > [[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. > -- Joshua Wiley Ph.D. Student Health Psychology University of California, Los Angeles ______________________________________________ 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.