Extract weights from prsCs function

Usage

prsCsWeights(
  stat,
  ldReference,
  fitRetention = c("none", "slim", "full"),
  methodArgs = PrsCsOptions()
)

Arguments

stat: A list of summary statistics: n (per-variant sample sizes) and the marginal effects as cor (standardized), z, b with seb, or b alone, taken as already standardized. The engine receives the standardized effects z / sqrt(n), with n the median of n.
ldReference: The LD reference the statistics' variants are matched to by stat$variantNames: a genotype panel (see readGenotypes), a GenotypeMixture or an LdMatrix. A genotype reference's LD is used through its standardized genotypes, never formed as a matrix.
fitRetention: How much of the fit is kept as the "fit" attribute of the returned weights: "none" (default) attaches nothing, "slim" and "full" attach the sampler's posterior estimates: betaEst, the local shrinkage parameters psiEst, the residual variance sigmaEst and the global shrinkage phiEst. This engine keeps no trimmable intermediates, so the two retaining levels behave alike here.
methodArgs: Options forwarded to the sampler, prsCs, built with PrsCsOptions. A bare list is refused: it cannot be checked against the engine, so a misspelled option would be silently ignored.

Value

A numeric vector of the posterior SNP coefficients. Unless fitRetention = "none", the sampler's estimates are attached as attribute "fit".

Examples

data(eqtlRegionExample)
X <- eqtlRegionExample$X[, 1:30]
y <- eqtlRegionExample$yRes
ss <- lapply(
  seq_len(ncol(X)), function(j) coef(summary(lm(y ~ X[, j])))[2, 1:2])
stat <- list(
  b = vapply(ss, `[`, numeric(1), 1L),
  seb = vapply(ss, `[`, numeric(1), 2L),
  n = rep(nrow(X), ncol(X)),
  variantNames = colnames(X)
)
w <- prsCsWeights(
  stat,
  LdMatrix(cor(X)),
  fitRetention = "slim",
  methodArgs = PrsCsOptions(maf = rep(0.3, ncol(X)))
)
attr(w, "fit")$phiEst
#> [1] 0.003201511