BLRM.fit.mwg: Bayesian Logistic Regression Model (BLRM) training

In lcw68/G3proj: BIOS 735 Project

Description

Performs Bayesian Logistic Regression Model training by sampling beta from posterior distribution with user specified parameters and data

Usage

BLRM.fit.mwg(
  Y0,
  X0,
  PriorVar,
  propSD0,
  nMC = 1000,
  nBI = 250,
  thin = 5,
  seed = 1
)

Arguments

Y0	vector of responses
X0	covariate matrix
PriorVar	variance of prior distribution of beta
propSD0	vector of standard deviations for normal proposal density
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 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)
propSD = rep(1,p)

## fit model;
test1 <- G3proj::BLRM.fit.mwg(Y0 = Y, X0 = X, PriorVar = 1000, propSD0 = propSD,
                      nMC = 500, nBI = 100, thin = 5)

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