lassosum2
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,
ncores = 1
)
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, 0.01, 0.1, 1)
.
Number of different lambdas to try (L1-regularization).
Default is 30
.
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 1000
.
Tolerance parameter for assessing convergence.
Default is 1e-5
.
Number of cores used. Default doesn't use parallelism. You may use nb_cores.
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.