On May 31, 2013, at 17:10 , Stefano Sofia wrote: > I find difficult to understand why in > lm(log(Y) ~ X) > Y is assumed lognormal. > I know that if Y ~ N then Z=exp(Y) ~ LN, and that if Y ~ LN then Z=log(Y) ~ N. > In > lm(log(Y) ~ X) > I assume Y ~ N(mu, sigma^2), and then exp(Y) would be distributed by a LN, > not l > og(Y). > Where is my mistake?
It is log(Y) that is assumed N(mu, sigma^2), and exp(log(Y)) is LN. > > Moreover, in > glm(Y ~ X, family=gaussian(link=log)) > the regression is > log(mu) = beta0 + beta1*X. > In > lm(log(Y) ~ X) > the regression is > exp(mu+(1/2)*sigma^2) = beta0 + beta1*X. > Correct? Probably not. (What is mu? If it is E(log(Y)), then it should just be just mu=beta0+beta1*X) -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ 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.
