On Sun, 15 Jul 2007, Tobias Verbeke wrote: > The survey package of Thomas Lumley has very broad functionality for the > analysis of data from complex sampling designs. Please find below the > homepage of the package (which is available on CRAN): > > http://faculty.washington.edu/tlumley/survey/ > > I don't think non-parametric one-way ANOVA is implemented
No. > but quoting > http://faculty.washington.edu/tlumley/survey/survey-wss.pdf > "Many features of the survey package result from requests from > unsatisfied users. > > For new methods the most important information is a reference > that gives sufficient detail for implementation. A data set is nice > but not critical." > Yes, and the details are especially non-obvious here. The test won't be small-sample exact, AFAICS, and it isn't clear whether the idea is to add weights to the influence function for the signed-rank test or to replace it with a design-based estimate of a population quantity. Often these approaches are equivalent, but they won't be in this case. It wouldn't have occured to me that people would want this. `Non-parametric' isn't really a relevant idea since design-based inference assumes a completely known model for the sampling indicators and doesn't even treat the data as random variables. All this goes to say that if there is a standard quantity that John wants, it will have resulted in part from a set of arbitrary decisions, and it won't be possible to reverse-engineer the estimator in the absence of a precise description. This is in contrast to apparently more complicated analyses such as calibration estimators for Cox models in case-cohort designs, which follow just by putting standard pieces together in an obvious way. -thomas ______________________________________________ [email protected] 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.
