Primo_missdata_pval: Estimate posterior probabilities for observations missing...

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

Description

For each SNP, estimates the posterior probability for each association pattern. Uses parameters estimated by a previous run of Primo_pval or Primo_chiMix to estimate probabilities for SNPs missing in one or more studies. P-values from non-missing studies are used as input. Utilizes parallel computing, when available.

Usage

Primo_missdata_pval(pvals, trait_idx, pis, Gamma, A, df_alt,
  par_size = 1)

Arguments

pvals	matrix of P-values from test statistics.
trait_idx	integer vector of the columns corresponding to non-missing phenotypes/studies.
pis	vector of the estimated proportion of observations belonging to each association pattern
Gamma	correlation matrix.
A	vector of scaling factors under the alternative distributions.
df_alt	vector of degrees of freedom approximated for the alternative distributions.
par_size	numeric value; specifies the number of workers for parallel computing (1 for sequential processing).

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, 5:21 p.m.