# GBJ: GBJ.R In GBJ: Generalized Berk-Jones Test for Set-Based Inference in Genetic Association Studies

## Description

Calculate the Generalized Berk-Jones test statistic and p-value.

## Usage

 `1` ```GBJ(test_stats, cor_mat = NULL, pairwise_cors = NULL) ```

## Arguments

 `test_stats` Vector of test statistics for each factor in the set (i.e. marginal test statistic for each SNP in a gene). `cor_mat` d*d matrix of the correlations between all the test statistics in the set, where d is the total number of test statistics in the set. You only need to specify EITHER cor_mat OR pairwise_cors. `pairwise_cors` A vector of all d(d-1)/2 pairwise correlations between the test statistics. You only need to specify EITHER cor_mat OR pairwise_cors.

## Value

A list with the elements:

 `GBJ` The observed Generalized Higher Criticism test statistic. `GBJ_pvalue` The p-value of this observed value, given the size of the set and correlation structure. `err_code` Sometimes if your p-value is very small (<10^(-12) usually), R/C++ do not have enough precision in their standard routines to calculate the number accurately. In these cases (and very rarely others) we switch to standard Berk-Jones instead (more stable numerically) and let you know with a message here.

## Examples

 ```1 2 3 4 5 6``` ```# Should return statistic = 0.9248399 and p_value = 0.2670707 set.seed(100) Z_vec <- rnorm(5) + rep(1,5) cor_Z <- matrix(data=0.2, nrow=5, ncol=5) diag(cor_Z) <- 1 GBJ(test_stats=Z_vec, cor_mat=cor_Z) ```

GBJ documentation built on March 26, 2020, 6:05 p.m.