# BayesPois: Bayesian Pois Regression In Bolstad2: Bolstad functions

## 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

 ```1 2 3 4 5``` ```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

 ```1 2``` ```data(poissonTest.df) results <- BayesPois(poissonTest.df\$y, poissonTest.df\$x) ```

Bolstad2 documentation built on May 29, 2017, 3:35 p.m.