Compute MCP-Penalized Weights from Summary Statistics

Fits MCP-penalized regression on the RSS objective, searching over a shrinkage grid s and lambda path. Model selection uses LD-quadratic pseudovalidation by default.

Usage

mcp_rss_weights(
  stat,
  LD,
  s = c(0.2, 0.5, 0.9, 1),
  gamma = 3,
  alpha = 1,
  selection = c("ld_quadratic", "min_fbeta"),
  ...
)

Arguments

stat

A list with $b (effect sizes) and $n (per-variant sample sizes).

LD

LD correlation matrix R (single matrix, NOT pre-shrunk).

s

Numeric vector of LD shrinkage parameters. Default: c(0.2, 0.5, 0.9, 1.0).

gamma

MCP concavity parameter. Default 3.

alpha

Elastic-net mixing (1 = pure L1). Default 1.

selection

Selection strategy: "ld_quadratic" (default) or "min_fbeta".

...

Additional arguments passed to penalized_rss().

Value

A numeric vector of SNP coefficient weights.