BlockGibbsSampler: The iterated block Gibbs sampler algorithm

In Kaukol/IBGS: Variable selection in ultrahigh dimensions using Gibbs sampler

The iterated block Gibbs sampler algorithm

Description

The iterated block Gibbs sampler algorithm

Usage

BlockGibbsSampler(
  y,
  x,
  n.iter = 3,
  n.models = 10,
  H = 30,
  kapp = 20,
  tau = 0.9,
  perm = TRUE,
  len = 250,
  k = 1,
  gamma = 0.5,
  info = c("AIC", "BIC", "AICc", "exBIC"),
  family = c("gaussian", "poisson", "binomial")
)

Arguments

y	the response variable
x	the predictors
n.iter	the number of iterations
n.models	the number of top selected models
H	the number of predictors in small groups, default is 30
kapp	the number of selected predictors in first step, default is 20
tau	the threshold to select the important predictors in second step, default is 0.9
perm	the permutation of Gibbs sampler, default is TRUE
len	the half number of generated samples, default is 250
k	the tuning parameter, default is 1
gamma	the parameter for extended BIC, default is 0.5
info	the selected model selection criterion from AIC, AICc, BIC and exBIC
family	the type of model from linear, logistic, poisson

Value

a list contains a summary of final result

Kaukol/IBGS documentation built on June 17, 2024, 2:37 p.m.