Samples from the posterior distribution

Usage

posterior_samples_multitrait(
  beta,
  se,
  eaf,
  R,
  k,
  omega,
  u,
  n,
  maxsize,
  tau0,
  r0,
  niter,
  burnin,
  p,
  seed = 456,
  excl.burnin = TRUE,
  a0 = 1,
  b0 = NULL,
  inds0 = NULL,
  standardize = TRUE,
  verbose = TRUE,
  clump = TRUE,
  clump_r2 = 0.99^2,
  check_ld = FALSE,
  ala = NULL
)

Arguments

beta: Vector of effect sizes.
se: Vector of standard errors.
eaf: Vector of effect allele frequencies.
R: LD matrix.
maxsize: The maximum number of causal variants.
tau0: Parameter tau.
r0: Parameter $r$.
niter: Number of iterations.
burnin: Number of burn-in samples.
p: Number of variants.
seed: Random seed.
excl.burnin: Should the burn-in be excluded?
a0: Hyperparameter a for the model size prior.
b0: Hyperparameter b for the model size prior.
inds0: Initial model indices (not used).
standardize: Should the effect sizes be standardised? Defaults to TRUE.
verbose: Verbose output.
clump: Whether to clump extremely highly correlated variants.
clump_r2: Clumping threshold for extremely highly correlated variants.
check_ld: Should the Z-scores be checked for LD discrepancy?
ala: Whether Approximate Laplace should be used?

Value

List.