Run the hierarchical mcmc model to infer priors
Usage
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 information 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 distribution) of beta
- beta_scale
prior for the scale of beta
- gamma_shape
prior for the shape (gamma distribution) of gamma
- gamma_scale
prior for the scale of gamma