Sorry, for the second question. I stated in a wrong way. My aim is the mean and sd of each new observation.
##### mean=fitted(prediction) ###### But I do not know how to get sd for each new observation. Any tips? Thanks Yi On Wed, Jul 21, 2010 at 2:29 PM, Yi <liuyi.fe...@gmail.com> wrote: > Hi, folks, > > Here are the codes: > > ############## > y=1:10 > x=c(1:9,1) > lin=lm(log(y)~x) ### log(y) is following Normal distribution > x=5:14 > prediction=predict(lin,newdata=x) ##prediction=predict(lin) > ############### > > 1. The codes do not work, and give the error message: Error in > eval(predvars, data, env) : > numeric 'envir' arg not of length one. But if I use the code after the > pound sign, it works. I mean the name of the newdata is x, why it does not > work though? > > > 2. Because the prediction is conducted for log(y). I need to get the > expected value of y, which is LN distribution, for the new data sets. I need > to know the expectation of log(y) and variance of log(y). > > ##### > mean=mean(prediction) > sd=sd(prediction) > mean_y=exp(mean+0.5*sd^2) ### formula from Normal to LN > ###### > > Is sd(prediction) the correct why to calculate the sigma of the prediction? > Or should I just use the value of Residual standard error from summary(lin)? > > Answer to either question will be appreciated! > > 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.