SSVS.Logistic: Stochastic Search Variable Selection (SSVS) for Logistic...

In lcw68/G3proj: BIOS 735 Project

Description

Performs SSVS for a logistic regression model with user specified parameters and data

Usage

SSVS.Logistic(
  Y0,
  X0,
  propSD0,
  c0,
  tau0,
  nMC = 1000L,
  nBI = 250L,
  thin = 5L,
  seed = 1L
)

Arguments

Y0	vector of responses
X0	covariate matrix without intercept
propSD0	vector of standard deviations for normal proposal density
c0	parameter for spike and slab prior of beta
tau0	parameter for spike and slab prior of beta
nMC	number of MCMC samples
nBI	number of burn-in samples
thin	number of samples to skip over in thinning
seed	set seed for random number generation

Value

a nested list of gamma samples, beta samples, and beta acceptance rates

Examples

## simulate data

set.seed(1)
N  = 100
p  = 10

X  = matrix(data = rnorm(N*p), nrow=N, ncol=p)
beta_true = c(rep(1,p/2),rep(0,p/2))
eta = X %*% beta_true
pi = exp(eta) / (1 + exp(eta))
Y  = rbinom(N,1,pi)
propSD0 = rep(.5,p)

## fit model;
test1 <- G3proj::SSVS.Logistic(Y0 = Y, X0 = X, propSD0, c0 = 10,
                               tau0 = 0.4, nMC = 1000, nBI = 100, thin=1, seed=1)

lcw68/G3proj documentation built on Dec. 21, 2021, 9:46 a.m.