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,
ind.corr = cols_along(corr),
ncores = 1
)
```

- 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`

: either tha GWAS sample size(s) when estimating`beta`

for a continuous trait, or in the case of a binary trait, this is`4 / (1 / n_control + 1 / n_case)`

; in the case of a meta-analysis, you should sum the effective sample sizes of each study instead of using the total numbers of cases and controls, see doi:10.1016/j.biopsych.2022.05.029 ; when using a mixed model, the effective sample size needs to be adjusted as well, see doi:10.1016/j.xhgg.2022.100136 .

- 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()`

.

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.