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Extract weights from lassosumRss with shrinkage grid search

Source: R/regularizedRegressionWrappers.R
lassosumRssWeights.Rd

Searches over a grid of shrinkage parameters s (default: c(0.2, 0.5, 0.9, 1.0), matching the original lassosum and OTTERS). For each s, the LD matrix is shrunk as (1-s)*R + s*I, then lassosumRss() is called across the lambda path. Candidate selection defaults to the LD-only quadratic pseudovalidation score $$\frac{c^T \beta}{\sqrt{\beta^T R \beta}}$$ evaluated on the supplied LD matrix R. This uses the same candidate beta path as lassosumRss(), but scores each candidate directly from summary-statistics correlation c and LD, without requiring genotype.

Usage

lassosumRssWeights(
  stat,
  LD,
  s = c(0.2, 0.5, 0.9, 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 shrinkage parameters to search over. Default: c(0.2, 0.5, 0.9, 1.0) following Mak et al (2017) and OTTERS.

selection

Selection strategy. Default "ld_quadratic" uses \(c^T \beta / \sqrt{\beta^T R \beta}\) on the supplied LD matrix. "min_fbeta" is retained as an explicit alternative for debugging.

...

Additional arguments passed to lassosumRss().

Value

A numeric vector of the posterior SNP coefficients at the best (s, lambda).

Details

The original lassosum pseudovalidation can be written as an LD quadratic score after centering and standardizing the reference matrix columns by the same per-variant scale: $$\mathrm{score}(\beta) = \frac{c^T \beta}{\sqrt{\beta^T R \beta}}.$$ This implementation therefore uses the supplied LD matrix directly for selection. min(fbeta) is retained only as an explicit debug option.

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))
)
LD <- cor(X)
lassosumRssWeights(stat, LD)
#>  [1] -0.048438809 -0.048206629 -0.022924444  0.005719588  0.005359918
#>  [6] -0.021442722 -0.016361411  0.030889502  0.009735165  0.009741973
#> [11] -0.017352850 -0.037738304 -0.028743496  0.000000000 -0.016995314
#> [16]  0.030671213  0.005594169  0.007675219  0.092565548  0.007598779
#> [21]  0.007513403 -0.021638059  0.007510310 -0.015755868 -0.028776244
#> [26] -0.021473335 -0.111441982 -0.028776116  0.092847617  0.022980710
#> attr(,"lassosum_selection")
#>           mode          index              s         lambda 
#> "ld_quadratic"            "1"          "0.2"        "1e-04"