Arguments p. numeric vector of p-values (possibly with NAs). Any other R object is coerced by as.numeric.. method. correction method. Can be abbreviated. n. number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing!, p: numeric vector of p-values (possibly with NAs). Any other R object is coerced by as.numeric.. method: correction method, a character string. Can be abbreviated. n: number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing!, I have a data frame with p.values and I want to adjust the p.values. I used this commad: Padjust = p.adjust(pvalues, fdr) pvalues is my data frame with missing values and fdr is the method that I, 7/6/2015 · This video is brought to you by the Quantitative Analysis Institute at Wellesley College as part of its Blended Learning Initiative. The video discusses adju…
This issue arose when I was trying to use p.adjust to run FDR adjustment on a vector of p-values. The problem was that many of the resultant p-values were identical. I thought this might be some quirk with my data, but I have reproduced this same problem with arbitrary input vectors.
points(p.adjust(ps, method=BH)~order(ps), add=TRUE, col=red) Generally, it seems that when a raw p-value has a difference <0.01 from the next-highest value, the corrected values come out identical. I can't understand why, though; I've read around and dug into the p.adjust code, but can't think what would cause this effect. Any insight would ...
