No - it is assumed to be conditionally normal, that is, normal conditional on the model. So you should be looking at the distributions of the residuals rather than of the response variable, as an indicator for whether or not the model assumptions are satisfied. Skewness in the residuals may or may not affect the outcome; it depends on what the purpose of the model is. Skewness in residuals may arise from a number of different problems with the model.
I hope that this helps, Andrew On Wed, Dec 11, 2013 at 1:55 AM, peyman <zira...@gmail.com> wrote: > Hi folks, > > I am using the lme package of R, and am wondering if it is assumed that > the dependent factor (what we fit for; y in many relevant texts) has to > have a normal Gaussian distribution? Is there any margins where some > skewness in the data is accepted and how within R itself one could check > distribution of the data? > > Thanks, > Peyman > > ______________________________________________ > 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. > -- Andrew Robinson Deputy Director, CEBRA Senior Lecturer in Applied Statistics Tel: +61-3-8344-6410 Department of Mathematics and Statistics Fax: +61-3-8344 4599 University of Melbourne, VIC 3010 Australia Email: a.robin...@ms.unimelb.edu.au Website: http://www.ms.unimelb.edu.au FAwR: http://www.ms.unimelb.edu.au/~andrewpr/FAwR/ SPuR: http://www.ms.unimelb.edu.au/spuRs/ [[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.
