compute_data_driven_covs: Compute data-driven covariance matrices.

In stephenslab/mr.mash.alpha: Multiple Regression with Multivariate Adaptive Shrinkage

Compute data-driven covariance matrices.

Description

Function to compute data-driven covariance matrices from summary statistics using PCA, FLASH and the sample covariance. These matrices are de-noised using Extreme Deconvolution.

Usage

compute_data_driven_covs(
  sumstats,
  subset_thresh = NULL,
  n_pcs = 3,
  flash_factors = c("default", "nonneg"),
  flash_remove_singleton = FALSE,
  Gamma = diag(ncol(sumstats$Bhat))
)

Arguments

sumstats	a list with two elements. 1 - Bhat, a numeric vector of regression coefficients. 2 - Shat, a numeric vector of of standard erros for the regression coefficients.
subset_thresh	scalar indicating the threshold for selecting the effects to be used for computing the covariance matrices based on false local sign rate (lfsr) for a response-by-response ash analysis.
n_pcs	indicating the number of principal components to be selected.
flash_factors	factors "default" to use flashr default function to initialize factors, currently udv_si. "nonneg" to implement a non-negative constraint on the factors
flash_remove_singleton	whether or not factors corresponding to singleton matrices should be removed from output.
Gamma	an r x r correlation matrix for the residuals; must be positive definite.

Value

A list containing the (de-noised) data-driven covariance matrices.

stephenslab/mr.mash.alpha documentation built on Feb. 7, 2025, 10:06 p.m.