[R] bootstrapping the lme model
spencer.graves at pdf.com
Mon Jul 14 18:34:42 CEST 2003
The real issue is not the distribution of the responses but the
distribution of errors assumed to be additive and normal. Before I
tried bootstrapping, I'd do other things first:
1. What are the response variable(s)? What do they look like on a
normal probability plot (qqnorm)? Might a transformation make the
hypothesis of additive normal errors more plausible?
2. What do the residuals look like in a normal probability plot?
I do the simple things first. If time and money permit and the
problem seems sufficiently important, then I investigate other
alternatives like bootstrapping.
Venables and Ripley, Modern Applied Statistics with S, discuss
bootstrapping, as does Frank Harrell, Regression Modeling Strategies.
hope this helps.
J.Illian at abertay.ac.uk wrote:
> Dear all,
> I have a data set o which I'd like to fit lme model. There are three factors
> one of whoich is nested. This should be easy to do using lme in R, but the
> problem ist that the data is highly non-normal. I was thinking about
> bootstrapping the distribution but don't have much experience of doing this
> in R and most references I find don't seem to go beyond the
> "two-sample-t-test" setting.
> Any suggestions are very welcome.
> Janine Illian
> lecturer in statistics
> School of Computing and Advanced Technologies
> University of Abertay Dundee
> Bell Street
> Dundee, DD1 1HG
> Scotland, UK
> Tel: +44-(0)1382-308488
> Fax: +44-(0)1382-308537
> R-help at stat.math.ethz.ch mailing list
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