mlr: Bayesian Multinomial Logistic Regression In bayesCL: Bayesian Inference on a GPU using OpenCL

Description

Inference for Bayesian multinomial logistic regression models by Gibbs sampling from the Bayesian posterior distribution.

Usage

 1 2 3 4 mlr(y, X, n=rep(1,nrow(as.matrix(y))), m.0=array(0, dim=c(ncol(X), ncol(y))), P.0=array(diag(0, ncol(X)), dim=c(ncol(X),ncol(X),ncol(y))), samp=1000, burn=500, float=0, device=0, parameters=NULL)

Arguments

 y an N x J-1 dimensional matrix; y_{ij} is the average response for category j at x_i. X an N x P dimensional design matrix; x_i is the ith row. n an N dimensional vector; n_i is the total number of observations at each x_i. m.0 a P x J-1 matrix with the β_j's prior means. P.0 a P x P x J-1 array of matrices with the β_j's prior precisions. samp the number of MCMC iterations saved. burn the number of MCMC iterations discarded. float a number representing the degree of precision to use: for single-precision floating point use 0, for or double-precision floating point use 1. device if no external pointer is provided to function, we can provide the ID of the device to use. parameters a 9 dimensional vector of parameters to tune the GPU implementation.

Details

Classic multinomial logistic regression for classifiction.

We assume that β_J = 0 for purposes of identification.

Value

mlr returns a list.

 beta a samp x P x J-1 array; the posterior sample of the regression coefficients. w a samp x N' x J-1 array; the posterior sample of the latent variable. WARNING: N' may be less than N if data is combined. y the response matrix–different than input if data is combined. X the design matrix–different than input if data is combined. n the number of samples at each observation–different than input if data is combined.