calc.p: Calculate P Values for Interactions Based on Permutations

Description Usage Arguments Value References

View source: R/calc.p.R


This function uses the perumtation results to calculate empirical p values for the variant-to-variant influences calculated by error.prop. It can also optionally adjust these p values using Holm's step-down procedure, false discovery rate (fdr), or local false discovery rate (lfdr).


calc.p(data.obj, pairscan.obj, 
pval.correction = c("holm", "fdr", "lfdr", "none"), n.cores = 2)



The object in which all results are stored. See read.population.


The object in which the results from the pairscan are stored. See pairscan.


One of "holm", "fdr", "lfdr" or "none", indicating whether the p value correction method used should be the Holm step-down procedure, false discovery rate, local false discovery, or no correction rate respectively.


An integer specifying the number of cores to be used in parallel processing.


The data object is returned with a new list with two elements. The elements correspond to the two directions of influence: marker1 to marker2 and marker2 to marker1. Each element contains a table with the source and target variants, the empirical p values, and the adjusted p values, along with the effect size, standard error and t statistic for each interaction.


Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, pages 65-70. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Societ. Series B (Methodological), 289-300. Liao, J.G., Lin, Y., Selvanayagam, Z.E., & Shih, W.J. (2004). A mixture model for estimating the local false discovery rate in DNA microarray analysis. Bioinformatics, 20(16), 2694-2701. doi:10.1093/bioinformatics/bth310

cape documentation built on May 2, 2019, 3:27 a.m.