Primo_missdata_tstat: Estimate posterior probabilities for observations missing...

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

Description

For each observation (e.g. SNP), estimates the posterior probability for each association pattern. Uses parameters estimated by a previous run of Primo_tstat or Primo_modT to estimate probabilities for SNPs missing in one or more studies. Utilizes parallel computing, when available.

Usage

Primo_missdata_tstat(betas, sds, dfs, trait_idx, mafs = NULL, N = NULL,
  pis, Gamma, prior_df, prior_var, unscaled_var, par_size = 1)

Arguments

betas	matrix of coefficient estimates.
sds	matrix of standard errors (for coefficient estimates).
dfs	vector or matrix of degrees of freedom.
trait_idx	integer vector of the columns corresponding to non-missing phenotypes/studies.
mafs	vector or matrix of minor allele frequencies (MAFs). If NULL, error variances will not be adjusted for MAF.
N	vector or matrix of number of subjects. Should be specified if mafs!=NULL.
pis	vector of the estimated proportion of observations belonging to each association pattern.
Gamma	correlation matrix.
prior_df	vector of the prior degrees of freedom for each marginal distribution.
prior_var	vector of the prior variance estimators for each marginal distribution.
unscaled_var	vector of the unscaled variance priors on non-zero coefficients for each marginal distribution.
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.
Tstat_mod	matrix of moderated t-statistics.
V_mat	matrix of scaling factors under the alternative distribution.
mdf_sd_mat	matrix of standard deviation adjustment according to moderated degrees of freedom:sqrt(df/(df-2)).
prior_df	vector of the prior degrees of freedom for each marginal distribution.
prior_var	vector of the prior variance estimators for each marginal distribution.
unscaled_var	vector of the unscaled variance priors on non-zero coefficients for each marginal distribution.

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.