Thanks Peter. I understand your point, and that there is potentially a high false discovery rate - but I'd expect the interesting data points (genes on a microarray) to be within that list too. The next step would be to filter based on some greater understanding of the biology...
Alternative approaches that come to mind are to look at the magnitude of the deviation - through Q-Q plot residuals, or to perform a linear regression on each row, and select those rows for which the coefficients fit predefined criteria. I'm still feeling my way into how to do this, though. Is there a better approach to identifying non-normal or skewed distributions that I am missing? Thanks for your advice... -- View this message in context: http://r.789695.n4.nabble.com/Finding-non-normal-distributions-per-row-of-data-frame-tp3259439p3260881.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
