Primo_pval: Estimate posterior probabilities of association patterns,...

In kjgleason/Primo: Package in R for Integrative Multi-Omics association analysis

Description

For each observation (e.g. SNP), estimates the posterior probability of each association pattern. Utilizes parallel computing, when available.

Usage

Primo_pval(pvals, alt_props, Gamma = NULL, tol = 0.001, par_size = 1)

Arguments

pvals	matrix of P-values from test statistics.
alt_props	vector of the proportions of test statistics from alternative densities.
Gamma	correlation matrix.
tol	numeric value specifying the tolerance threshold for convergence.
par_size	numeric value specifying the number of workers for parallel computing (1 for sequential processing).

Details

The following are additional details describing the input arguments (for m SNPs/observations measured in d studies):

pvals	m x d matrix.
alt_props	vector of length d.
Gamma	d x d matrix.
	If NULL, will be estimated using observations where all p < 5.7e-7.

Value

A list with the following elements:

post_prob	matrix of posterior probabilities (each column corresponds to an association pattern).
pis	vector of estimated proportion of observations belonging to each association pattern.
D_mat	matrix of densities under each association pattern.
Gamma	correlation matrix.
chi_mix	matrix of -2log(P)-values.
A	vector of scaling factors under the alternative distributions.
df_alt	vector of degrees of freedom approximated for the alternative distributions.

The main element of interest for inference is the posterior probabilities matrix, post_prob. The estimated proportion of observations belonging to each association pattern, pis, may also be of interest. The remaining elements are returned primarily for use by other functions.

kjgleason/Primo documentation built on Sept. 7, 2021, 3:58 a.m.