MGLMtune-class

A class containing the results from the <code>MGLMtune</code>.

classes

Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

Juhyun Kim

MGLM

Multivariate Response Generalized Linear Models

MGLMtune-class function

<dl>
<dt><code>call</code></dt>
<dd>object of class <code>"call"</code>.</dd></dl>
<dt><code>select</code></dt>
<dd>object of class <code>"MGLMsparsereg"</code>,
regularized regression results given by the optimal tuning parameter.</dd>
<dt><code>path</code></dt>
<dd>object of class <code>"data.frame"</code>,
the BIC, AIC, log-likelihood and degrees of freedom given each tuning parameter.</dd>
<dt><code>select.list</code></dt>
<dd>object of class <code>"list"</code>,
the regularized regression results at each tuning grid point.</dd>

Slots

Author

Class "MGLMtune" — MGLMtune-class

<dl>
<dt><code>call</code></dt>
<dd>object of class <code>"call"</code>.</dd>


<dt><code>select</code></dt>
<dd>object of class <code>"MGLMsparsereg"</code>,
regularized regression results given by the optimal tuning parameter.</dd>


<dt><code>path</code></dt>
<dd>object of class <code>"data.frame"</code>,
the BIC, AIC, log-likelihood and degrees of freedom given each tuning parameter.</dd>


<dt><code>select.list</code></dt>
<dd>object of class <code>"list"</code>,
the regularized regression results at each tuning grid point.</dd>


</dl>

MGLMtune-class: Class `"MGLMtune"`

Description

Arguments

Slots

Author

Examples