[BioC] p-value adjustment

Matthew Hannah Hannah at mpimp-golm.mpg.de
Thu Mar 24 14:31:10 CET 2005

One thing to add on this point that might be of interest is that an
initial look at p.adjust
p.adjust(p, method = p.adjust.methods, n = length(p))
would suggest that including NAs would result in more conservative
correction as n > # of p-values, but it doesn't (at least for fdr) as
the NAs are ordered below the minimum p-value from your vector in the
calculation. So if you fdr correct with the NAs present then you get
more significant p-values than you should and the results are
meaningless - and there's no error message.

Obviously, the nice work by the developers will correct this.

>>Wenbin Liu wnbnl at yahoo.com
>>Wed Mar 23 23:51:28 CET 2005
>>Dear list,
>>I'm puzzled by the R function p.adjust with NA values
>>in the first argument. The adjustment gives different
>>result with and without the NAs.
>Please refer to the extensive recent discussion on this topic on the 
>R-devel mailing list.
>>  The question then is,
>>which approach should one take: with or without NAs in
>>the first argument?
>The limma package functions remove NAs before using p.adjust(), and I 
>believe this is virtually always correct in the microarray context.
>>Thanks a lot!

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