Run the hierarchical Metropolis Hastings model to infer priors

run_metrop_priors(
  multi.dat,
  covar = FALSE,
  covar_vec = NULL,
  is_covar_categorical = FALSE,
  nits = 10000,
  thin = 1,
  posterior = FALSE,
  avg_pik = TRUE,
  avg_posterior = TRUE,
  pik = FALSE,
  alpha_mean = -10,
  alpha_sd = 0.5,
  beta_shape = 2,
  beta_scale = 2,
  gamma_shape = 2,
  gamma_scale = 2
)

Arguments

multi.dat: matrix of bf values, rows=traits, named columns=("lBF.Ha","lBF.Hc","nsnps")
covar: whether to include covariates
covar_vec: vector of covariates
is_covar_categorical: only two categories supported (default=FALSE) - Experimental
nits: number of iterations
thin: burnin
posterior: default: FALSE, estimate posterior probabilities of the hypotheses
avg_pik: default: FALSE, estimate the average of the pik
avg_posterior: default: FALSE, estimate the average of the posterior probabilities of the hypotheses
pik: default: FALSE, inferred prior probabilities
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

List containing the posterior distribution of the parameters alpha, beta, gamma (if covariate included) and the loglikelihood

if avg_posterior=TRUE matrix with average of all the posterior probabilities of Hn, Ha and Hc

if avg_pik=TRUE matrix with average of all the priors: pn, pa and pc

data, nits and thin contain the input data, number of iterations and burnin respectively specified for the hierarchical model