order_kruskal

This function orders all features in terms of Kruskal-Wallis test p-value.

internal

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

order_kruskal function

<dl><dt>X</dt>
<dd>Input matrix, of dimension <code>nobs</code> by <code>nvars</code>; each row is an observation vector.</dd>
<dt>y</dt>
<dd>Vector of response variables.</dd></dl>

Arguments

Order features by Kruskal-Wallis test — order_kruskal

<dl>

<dt>X</dt>
<dd>Input matrix, of dimension <code>nobs</code> by <code>nvars</code>; each row is an observation vector.</dd>


<dt>y</dt>
<dd>Vector of response variables.</dd>

</dl>

order_kruskal: Order features by Kruskal-Wallis test

Description

Usage

Value

Arguments

Examples