Bayesian Pois Regression

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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

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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

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