John Fox has a nice explanation here: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-bootstrapping.pdf
-Ista On Wed, Oct 21, 2009 at 6:38 AM, Charlotta Rylander <z...@nilu.no> wrote: > Hello, > > We are a group of PhD students working in the field of toxicology. Several > of us have small data sets with N=10-15. Our research is mainly about the > association between an exposure and an effect, so preferrably we would like > to use linear regression models. However, most of the time our data do not > fulfill the model assumptions for linear models ( no normality of y-varible > achieved even after log transformation). We have been told that we can use > bootstrapping to derive a confidence interval for the original parameter > estimate ( Beta 1) from the linear regression model and if the confidence > interval do not include 0, we can "trust" the result from the original > linear model ( of couse only if a scatter plot of the variables looks ok). > What is your opinion about this method? Is that ok? I have problems > understanding how it is possible to resample several times from an already > poor distribution ( that do not fulfill the model assumptions for linear > models) to achieve a confidence interval that "validates" the use of these > linear models? I would really appriciate a simple explanation about this! > > Many thanks, > > Charlotta Rylander > > [[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. > -- Ista Zahn Graduate student University of Rochester Department of Clinical and Social Psychology http://yourpsyche.org ______________________________________________ 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.