predict.htlr.fit

Similar to other predict methods, this function returns predictions from a fitted <code>htlrfit</code> object.

Efficient Bayesian multinomial logistic regression based on heavy-tailed
(hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters
is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and
Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed
description of the method: Li and Yao (2018),
Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.

Longhai Li

HTLR

Bayesian Logistic Regression with Heavy-Tailed Priors

Steven Liu

predict.htlr.fit function

<dl><dt>object</dt>
<dd>A fitted model object with S3 class <code>htlrfit</code>.</dd>
<dt>newx</dt>
<dd>A Matrix of values at which predictions are to be made.</dd>
<dt>type</dt>
<dd>Type of prediction required. Type "response" gives the fitted probabilities.
Type "class" produces the class label corresponding to the maximum probability.</dd>
<dt>...</dt>
<dd>Advanced options to specify the Markov chain iterations used for inference. 
See <code>htlr_predict</code>.</dd></dl>

Arguments

Make Prediction on New Data — predict.htlr.fit

<dl>

<dt>object</dt>
<dd>A fitted model object with S3 class <code>htlrfit</code>.</dd>


<dt>newx</dt>
<dd>A Matrix of values at which predictions are to be made.</dd>


<dt>type</dt>
<dd>Type of prediction required. Type "response" gives the fitted probabilities.
Type "class" produces the class label corresponding to the maximum probability.</dd>


<dt>...</dt>
<dd>Advanced options to specify the Markov chain iterations used for inference. 
See <code>htlr_predict</code>.</dd>

</dl>

predict.htlr.fit: Make Prediction on New Data

Description

Usage

Value

Arguments