Run the hierarchical mcmc model to infer priors

Run the hierarchical mcmc model to infer priors

metrop_run(
  lbf_mat,
  nsnps,
  covar_vec,
  covar = FALSE,
  nits = 10000L,
  thin = 1L,
  alpha_mean = -10,
  alpha_sd = 0.5,
  beta_shape = 2,
  beta_scale = 2,
  gamma_shape = 2,
  gamma_scale = 2
)

Arguments

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: Should the covariate inflormation be used? default: False

nits

Number of iterations run in mcmc

thin

thinning

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

named list of log likelihood (ll) and parameters: alpha, beta and gamma