BayesPois: Bayesian Pois Regression

In Bolstad2: Bolstad Functions

Bayesian Pois Regression

Description

Performs Metropolis Hastings on the logistic regression model to draw sample from posterior. Uses a matched curvature Student's t candidate generating distribution with 4 degrees of freedom to give heavy tails.

Usage

BayesPois(
  y,
  x,
  steps = 1000,
  priorMean = NULL,
  priorVar = NULL,
  mleMean = NULL,
  mleVar,
  startValue = NULL,
  randomSeed = NULL,
  plots = FALSE
)

Arguments

y	the binary response vector
x	matrix of covariates
steps	the number of steps to use in the Metropolis-Hastings updating
priorMean	the mean of the prior
priorVar	the variance of the prior
mleMean	the mean of the matched curvature likelihood
mleVar	the covariance matrix of the matched curvature likelihood
startValue	a vector of starting values for all of the regression coefficients including the intercept
randomSeed	a random seed to use for different chains
plots	Plot the time series and auto correlation functions for each of the model coefficients

Value

A list containing the following components:

beta	a data frame containing the sample of the model coefficients from the posterior distribution
mleMean	the mean of the matched curvature likelihood. This is useful if you've used a training set to estimate the value and wish to use it with another data set
mleVar	the covariance matrix of the matched curvature likelihood. See mleMean for why you'd want this

Examples

data(poissonTest.df)
results = BayesPois(poissonTest.df$y, poissonTest.df$x)

Bolstad2 documentation built on April 11, 2022, 5:08 p.m.