snp_ldpred2_inf  R Documentation 
LDpred2. Tutorial at https://privefl.github.io/bigsnpr/articles/LDpred2.html.
snp_ldpred2_inf(corr, df_beta, h2)
snp_ldpred2_grid(
corr,
df_beta,
grid_param,
burn_in = 50,
num_iter = 100,
ncores = 1,
return_sampling_betas = FALSE,
ind.corr = cols_along(corr)
)
snp_ldpred2_auto(
corr,
df_beta,
h2_init,
vec_p_init = 0.1,
burn_in = 500,
num_iter = 200,
sparse = FALSE,
verbose = FALSE,
report_step = num_iter + 1L,
allow_jump_sign = TRUE,
shrink_corr = 1,
use_MLE = TRUE,
alpha_bounds = c(1.5, 0.5),
ind.corr = cols_along(corr),
ncores = 1
)
corr 
Sparse correlation matrix as an SFBM.
If 
df_beta 
A data frame with 3 columns:

h2 
Heritability estimate. 
grid_param 
A data frame with 3 columns as a grid of hyperparameters:

burn_in 
Number of burnin iterations. 
num_iter 
Number of iterations after burnin. 
ncores 
Number of cores used. Default doesn't use parallelism. You may use nb_cores. 
return_sampling_betas 
Whether to return all sampling betas (after
burnin)? This is useful for assessing the uncertainty of the PRS at the
individual level (see \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1101/2020.11.30.403188")}).
Default is 
ind.corr 
Indices to "subset" 
h2_init 
Heritability estimate for initialization. 
vec_p_init 
Vector of initial values for p. Default is 
sparse 
In LDpred2auto, whether to also report a sparse solution by
running LDpred2grid with the estimates of p and h2 from LDpred2auto, and
sparsity enabled. Default is 
verbose 
Whether to print "p // h2" estimates at each iteration. Disabled when parallelism is used. 
report_step 
Step to report sampling betas (after burnin and before
unscaling). Nothing is reported by default. If using 
allow_jump_sign 
Whether to allow for effects sizes to change sign in
consecutive iterations? Default is 
shrink_corr 
Shrinkage multiplicative coefficient to apply to offdiagonal
elements of the correlation matrix. Default is 
use_MLE 
Whether to use maximum likelihood estimation (MLE) to estimate
alpha and the variance component (since v1.11.4), or assume that alpha is
1 and estimate the variance of (scaled) effects as h2/(m*p), as it was
done in earlier versions of LDpred2auto (e.g. in v1.10.8). Default is 
alpha_bounds 
Boundaries for the estimates of 
For reproducibility, set.seed()
can be used to ensure that two runs of
LDpred2 give the exact same results (since v1.10).
snp_ldpred2_inf
: A vector of effects, assuming an infinitesimal model.
snp_ldpred2_grid
: A matrix of effect sizes, one vector (column)
for each row of grid_param
. Missing values are returned when strong
divergence is detected. If using return_sampling_betas
, each column
corresponds to one iteration instead (after burnin).
snp_ldpred2_auto
: A list (over vec_p_init
) of lists with
$beta_est
: vector of effect sizes (on the allele scale); note that
missing values are returned when strong divergence is detected
$beta_est_sparse
(only when sparse = TRUE
): sparse vector of effect sizes
$postp_est
: vector of posterior probabilities of being causal
$corr_est
, the "imputed" correlations between variants and phenotypes,
which can be used for postQCing variants by comparing those to
with(df_beta, beta / sqrt(n_eff * beta_se^2 + beta^2))
$sample_beta
: sparse matrix of sampling betas (see parameter report_step
),
not on the allele scale, for which you need to multiply by
with(df_beta, sqrt(n_eff * beta_se^2 + beta^2))
$path_p_est
: full path of p estimates (including burnin);
useful to check convergence of the iterative algorithm
$path_h2_est
: full path of h2 estimates (including burnin);
useful to check convergence of the iterative algorithm
$path_alpha_est
: full path of alpha estimates (including burnin);
useful to check convergence of the iterative algorithm
$h2_est
: estimate of the (SNP) heritability (also see coef_to_liab)
$p_est
: estimate of p, the proportion of causal variants
$alpha_est
: estimate of alpha, the parameter controlling the
relationship between allele frequencies and expected effect sizes
$h2_init
and $p_init
: input parameters, for convenience
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