![]() ![]() The wild type human caspase-2 is a dimer of heterodimers generated by autocatalytic processing which is required for its enzymatic activity. # P P.permuted P.adjusted.perm P.adjusted. Caspase-2 is the most specific protease of all caspases and therefore highly suitable as tag removal enzyme creating an authentic N-terminus of overexpressed tagged proteins of interest. P.adjusted.BH = round(p.adjust(p, method = "BH"), 4) This example also shows the discrepancy between the permutation-adjusted P values and Benjamini-Hochberg-adjusted P values. This happens also with a higher number of permutations, e.g. Benjamini-Hochberg-adjusted P"Īn example where permutation-adjusted P values increase, but (unadjusted) permuted P values decrease: Sum(x <= x) / length(x) # x is the original P valueĬalculate P values adjusted for multiple comparisons using permutation testing:īased on this link. Matrix(, nrow = n.perm, ncol = length(p)) ![]() P, # Original P values included as one of the permutations. Wilcox.test(x ~ Ionosphere $Class)$p.value # Column 33: binary dependent variable ('Class': bad/good).Ĭalculate original and permuted P values: # Calculate original P values: # Columns 1-32: continuous independent variables. Ionosphere <- Ionosphere # Remove factor variables. Here is the code: Prepare example data: library(mlbench) why P values adjusted for multiple comparisons using permutation testing do not always increase monotonically with (unadjusted) permuted P values?.if I did something wrong, as contrary to what I expected, P values adjusted for multiple comparisons using permutation testing are higher than e.g.whether the code is correct for calculating P values adjusted for multiple comparisons using permutation testing?.I did not manage to find an R code to do it, so I wrote it myself and used example data (see below). As some of my predictors are correlated, I understand that procedures such as Benjamini-Hochberg might not be valid, so I would like to use permutation testing to adjust for multiple comparisons. I have a number of continuous predictors (biomarker measurements) which I would like to test for association with a binary outcome variable (disease status), adjusting for multiple comparisons. ![]()
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