dear all, I recently came across the following issue and I was not sure whether it is intentionally or not: using p.adjust to adjust p-values for multiple hypothesis testing using the method from Benjamini and Hochberg removes all NA values from the input vector and does not account for them in the adjustment, i.e. in a vector of 23 p-values with 20 of them being NA it adjusts the 3 non-NA p-values as if there had only been 3 tests to adjust for (see example). I was not aware of that behaviour, and also implementations like the one in Bioconductor's multtest package handle NAs differently. If this behaviour is intentionally I would appreciate if a related note could be added to the help page.
Example: x <- c( 0.001, 0.01, 0.02, rep( NA, 20 ) ) p.adjust( x, method="BH" ) [1] 0.003 0.015 0.020 NA NA NA NA NA NA NA NA NA [13] NA NA NA NA NA NA NA NA NA NA NA p.adjust( x, method="BH", n=length( x ) ) [1] 0.0230000 0.1150000 0.1533333 NA NA NA NA [8] NA NA NA NA NA NA NA [15] NA NA NA NA NA NA NA [22] NA NA in the default settings (without specifying n, i.e. n=length(p)) the value of n is determined after all NAs have been removed from the p-value vector p. cheers, jo my R: > sessionInfo() R version 3.1.2 (2014-10-31) Platform: x86_64-apple-darwin14.0.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base > ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
