Jenushka <jhazlehu <at> tulane.edu> writes: > > I'm a beginning R user. > > The data: Volume of nectar in flowers under 4 different treatments, > nested > for individual (measures were taken mutliple times from different > flowers of > the same individual- never the same flower).
This is really more of a statistical question than an R question, and might get more informed answers at r-sig-ecol...@r-project.org, but: > > Specs: 54% of the data = 0. Variance=3.89. Mean=1.03. Sample size per > treatment varies, all are >20. > > I am trying to determine if treatment had any impact on nectar volume. > Since it will be hard to transform the data to normality (since you have a big pile of responses equal to exactly zero), one possibility would be a two-stage model: fit a binomial GL(M)M for zero vs non-zero, then fit a linear (mixed) model to the (probably log-transformed) positive responses. Alternatively you could ignore the distribution and use a randomization (permutation or bootstrap) approach to get reasonable p-values/confidence intervals, although you'll have to be careful to do the randomization respecting the grouping by individual. Ben Bolker ______________________________________________ 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.