It depends on what you are after. I am by no means a wunderkind when it comes to transformation, but in the package vegan type ?wisconsin and that should give you a start, but if you know what transformations you would like to preform then apply should do what you need with whatever transformation you are trying to use.
Stephen Sefick On Wed, May 27, 2009 at 5:26 AM, Hollix <holger.steinm...@web.de> wrote: > > Hello folks, > > many multivariate anayses (e.g., structural equation modeling) require > multivariate normal distributions. > Real data, however, most often significantly depart from the multinormal > distribution. Some researchers (e.g., Yuan et al., 2000) have proposed a > multivariate transformation of the variables. > > Can you tell me, if and how such a transformation can be handeled in R? > > Thanks in advance. > With best regards > Holger > > > --------------- > Yuan, K.-H., Chan, W., & Bentler, P. M. (2000). Robust transformation with > applications to structural equation modeling. British Journal of > Mathematical and Statistical Psychology, 53, 31–50. > -- > View this message in context: > http://www.nabble.com/Multivariate-Transformations-tp23739013p23739013.html > Sent from the R help mailing list archive at Nabble.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. > -- Stephen Sefick Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis ______________________________________________ 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.
