BayesLogistic {Bolstad2}R Documentation

Bayesian Logistic Regression

Description

Performas 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

BayesLogistic(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(logisticTest.df)
BayesLogistic(logisticTest.df$y, logisticTest.df$x)

[Package Bolstad2 version 1.0-26 Index]