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

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.

See Also

rpg,lasso

Examples

## Use the iris dataset.
data(iris)
N <- nrow(iris)
P <- ncol(iris)
J <- nlevels(iris$Species)

X     <- model.matrix(Species ~ ., data=iris);
y.all <- model.matrix(~ Species - 1, data=iris);
y     <- y.all[,-J];

out <- mlr(y, X, samp=1000, burn=100, device=0);

bayesCL documentation built on May 2, 2019, 3:43 p.m.