Dear Eliot, One way to cope with the uncertainty on the inputs in an analysis is vary these inputs by some amount (like +- 1 standard deviation) and rerun your analysis. The spread of the result tells you then how robust your analysis is. Pay attention that the inputs may be varied in an independent way if they ARE independent, if they highly correlated you may prefer to vary them simultaneously.
Hope that helps, Regards, Xavier 2013/7/4 Eliot Miller <eliotmil...@umsl.edu> > Hello all, > > I have been trying to get something to work in a number of different > packages and with a number of different approaches today that I couldn't > get to run in a believable way. Before I spend another day on this, I was > wondering what people think about the idea in general. > > I have a dataset of disease prevalence across ~100 species. There are ~2000 > individuals total across the dataset, with >4 individuals per species. > Prevalence per individual is coded as 0 or 1. I am interested in the > phylogenetic signal of disease prevalence across the species. One approach > that works is to simply calculate prevalence as the species-specific mean, > i.e. if 3 individuals of 6 for a species had the disease, the prevalence > would be 3/6 = 0.5. Then one can use these values with e.g. phylosig() (I > arcsin sqrt transformed these proportions here). Like the few other > published tests of phylogenetic signal in disease prevalence, there is > little signal here. I could leave it at that, because in general there are > very low detections in this dataset and it's probably not ideally suited to > address this question anyhow. > > That aside however, because not all individuals of a given species always > have the disease, I wanted to incorporate "measurement error". So, based on > the calculation for SE for binary data from the site: > > http://www.researchgate.net/post/Can_standard_deviation_and_standard_error_be_calculated_for_a_binary_variable > , > I also calculated a species-specific SEs as the > sqrt(mean(prevalence)*((1- > mean(prevalence))/individuals)). > > What do people think about this? It's hardly measurement error in the sense > we normally mean it. On the other hand, I think it would be neat if there > were some way to account for variation among individuals in prevalence, and > the influence this has on phylogenetic signal. > > Cheers, > Eliot > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-phylo mailing list - R-sig-phylo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo > Searchable archive at > http://www.mail-archive.com/r-sig-phylo@r-project.org/ > -- *--------------------------------------- Xavier Prudent * * Computational biology and evolutionary genomics * * * *Guest scientist at the Max-Planck-Institut für Physik komplexer Systeme* *(MPI-PKS)* *Noethnitzer Str. 38* *01187 Dresden * * * *Max Planck-Institute for Molecular Cell Biology and Genetics* * (MPI-CBG) * * Pfotenhauerstraße 108 * * 01307 Dresden * * * * Phone: +49 351 210-2621 * *Mail: prudent [ at ] mpi-cbg.de **---------------------------------------* [[alternative HTML version deleted]]
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