snp_lassosum2: lassosum2

In bigsnpr: Analysis of Massive SNP Arrays

lassosum2

Description

lassosum2

Usage

snp_lassosum2(
  corr,
  df_beta,
  delta = c(0.001, 0.01, 0.1, 1),
  nlambda = 30,
  lambda.min.ratio = 0.01,
  dfmax = 2e+05,
  maxiter = 1000,
  tol = 1e-05,
  ind.corr = cols_along(corr),
  ncores = 1
)

Arguments

corr	Sparse correlation matrix as an SFBM. If corr is a dsCMatrix or a dgCMatrix, you can use as_SFBM(corr).
df_beta	A data frame with 3 columns: ⁠$beta⁠: effect size estimates ⁠$beta_se⁠: standard errors of effect size estimates ⁠$n_eff⁠: sample size when estimating beta (in the case of binary traits, this is 4 / (1 / n_control + 1 / n_case))
delta	Vector of shrinkage parameters to try (L2-regularization). Default is c(0.001, 0.01, 0.1, 1).
nlambda	Number of different lambdas to try (L1-regularization). Default is 30.
lambda.min.ratio	Ratio between last and first lambdas to try. Default is 0.01.
dfmax	Maximum number of non-zero effects in the model. Default is 200e3.
maxiter	Maximum number of iterations before convergence. Default is 1000.
tol	Tolerance parameter for assessing convergence. Default is 1e-5.
ind.corr	Indices to "subset" corr, as if this was run with corr[ind.corr, ind.corr] instead. No subsetting by default.
ncores	Number of cores used. Default doesn't use parallelism. You may use nb_cores.

Value

A matrix of effect sizes, one vector (column) for each row in ⁠attr(<res>, "grid_param")⁠. Missing values are returned when strong divergence is detected.

bigsnpr documentation built on March 31, 2023, 10:37 p.m.