lassosum2

snp_lassosum2(
  corr,
  df_beta,
  delta = signif(seq_log(0.001, 3, 6), 1),
  nlambda = 20,
  lambda.min.ratio = 0.01,
  dfmax = 2e+05,
  maxiter = 500,
  tol = 1e-05,
  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, 0.005, 0.02, 0.1, 0.6, 3).

nlambda

Number of different lambdas to try (L1-regularization). Default is 20.

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 500.

tol

Tolerance parameter for assessing convergence. Default is 1e-5.

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.