Thank you so much for the explanation. That was very helpful! :-) Thanks!
Brittany > On Jul 16, 2015, at 6:16 PM, John Fox <j...@mcmaster.ca> wrote: > > Dear Brittany, > > On Thu, 16 Jul 2015 17:35:38 -0600 > Brittany Demmitt <demmi...@gmail.com> wrote: >> Hello, >> >> I have a series of 40 variables that I am trying to transform via the boxcox >> method using the powerTransfrom function in R. I have no zero values in any >> of my variables. When I run the powerTransform function on the full data >> set I get the following warning. >> >> Warning message: >> In sqrt(diag(solve(res$hessian))) : NaNs produced >> >> However, when I analyze the variables in groups, rather than all 40 at a >> time I do not get this warning message. Why would this be? And does this >> mean this warning is safe to ignore? >> > > No, it is not safe to ignore the warning, and the problem has nothing to do > with non-positive values in the data -- when you say that there are no 0s in > the data, I assume that you mean that the data values are all positive. The > square-roots of the diagonal entries of the Hessian at the (pseudo-) ML > estimates are the SEs of the estimated transformation parameters. If the > Hessian can't be inverted, that usually implies that the maximum of the > (pseudo-) likelihood isn't well defined. > > This isn't surprising when you're trying to transform as many as 40 variables > at a time to multivariate normality. It's my general experience that people > often throw their data into the Box-Cox black box and hope for the best > without first examining the data, and, e.g., insuring a reasonable ratio of > maximum/minimum values for each variable, checking for extreme outliers, etc. > Of course, I don't know that you did that, and it's perfectly possible that > you were careful. > >> I would like to add that all of my lambda values are in the -5 to 5 range. >> I also get different lambda values when I analyze the variables together >> versus in groups. Is this to be expected? >> > > Yes. It's very unlikely that both are right. If, e.g., the variables are > multivariate normal within groups then their marginal distribution is a > mixture of multivariate normals, which almost surely isn't itself normal. > > I hope this helps, > John > > ------------------------------------------------ > John Fox, Professor > McMaster University > Hamilton, Ontario, Canada > http://socserv.mcmaster.ca/jfox/ > > >> Thank you so much! >> >> Brittany >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> 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. > > > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.