Dear Jude I'm trying to reconcile where you say "it doesn't meet the assumption of homogeneity of variances" with "sample size within fields is small and unequal". With small sample sizes you can easily generate dummy samples with widely differing variances even where the process generating the data has a single variance. It's difficult to say without more information, but I'd be tempted just to use a linear mixed model to analyse the data.
regards Mike D >>> "Jude Phillips" <[EMAIL PROTECTED]> 19/11/2008 22:41 >>> Hi, I'm trying to analyse a dataset on the size of beetles collected in different crop types. Crop type is the fixed effect, and field is a random factor, nested in crop. Although my data is normal after transformation, it doesn't meet the assumption of homogeneity of variances. In addition, sample size within fields is small and unequal (n=2-10). So, I'm trying to figure out if I can run a nested Kruskal Wallis type analysis, since this is more robust to both problems than an ANOVA, even with permutation. I've been told it might be possible to run such an analysis within the coin package, but I can't figure out how to do this -methods for blocked designs are mentioned in the documentation, but not nested designs. I'd appreciate any help or thoughts! Cheers, Jude Phillips _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- This message (and any attachments) is for the recipient ...{{dropped:6}} _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
