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
)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.
Sparse correlation matrix as an SFBM.
If corr is a dsCMatrix or a dgCMatrix, you can use as_SFBM(corr).
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))
Vector of shrinkage parameters to try (L2-regularization).
Default is c(0.001, 0.005, 0.02, 0.1, 0.6, 3).
Number of different lambdas to try (L1-regularization).
Default is 20.
Ratio between last and first lambdas to try.
Default is 0.01.
Maximum number of non-zero effects in the model.
Default is 200e3.
Maximum number of iterations before convergence.
Default is 500.
Tolerance parameter for assessing convergence.
Default is 1e-5.
Number of cores used. Default doesn't use parallelism. You may use nb_cores.