MGLMfit-class

A class containing the model fitting results from the <code>MGLMfit</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

Yiwen Zhang

Hua Zhou

Magdalena Czapiewska

MGLMfit-class function

<dl>
<dt><code>estimate</code></dt>
<dd>object of class <code>"vector"</code>, containing the parameter estimates.</dd></dl>
<dt><code>SE</code></dt>
<dd>object of class <code>"vector"</code>,
containing the standard errors of the estimates.</dd>
<dt><code>vcov</code></dt>
<dd>object of class <code>"matrix"</code>,
the variance covariance matrix of the parameter estimates.</dd>
<dt><code>logL</code></dt>
<dd>object of class <code>"numeric"</code>, 
the fitted log likelihood.</dd>
<dt><code>BIC</code></dt>
<dd>object of class <code>"numeric"</code>, 
Bayesian information criterion.</dd>
<dt><code>AIC</code></dt>
<dd>object of class <code>"numeric"</code>,
Akaike information criterion.</dd>
<dt><code>LRTpvalue</code></dt>
<dd>object of class <code>"numeric"</code>,
likelihood ratio test p value.</dd>
<dt><code>gradient</code></dt>
<dd>object of class <code>"numeric"</code> or <code>"matrix"</code>,
containing the gradient.</dd>
<dt><code>iter</code></dt>
<dd>object of class <code>"numeric"</code>, 
number of iteration used.</dd>
<dt><code>distribution</code></dt>
<dd>object of class <code>"character"</code>,
the distribution fitted.</dd>
<dt><code>fitted</code></dt>
<dd>object of class <code>"vector"</code>,
the fitted mean of each category.</dd>
<dt><code>LRT</code></dt>
<dd>object of class <code>"numeric"</code>, 
the likelihood ratio test statistic.</dd>

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Author

Class "MGLMfit" — MGLMfit-class

<dl>
<dt><code>estimate</code></dt>
<dd>object of class <code>"vector"</code>, containing the parameter estimates.</dd>


<dt><code>SE</code></dt>
<dd>object of class <code>"vector"</code>,
containing the standard errors of the estimates.</dd>


<dt><code>vcov</code></dt>
<dd>object of class <code>"matrix"</code>,
the variance covariance matrix of the parameter estimates.</dd>


<dt><code>logL</code></dt>
<dd>object of class <code>"numeric"</code>, 
the fitted log likelihood.</dd>


<dt><code>BIC</code></dt>
<dd>object of class <code>"numeric"</code>, 
Bayesian information criterion.</dd>


<dt><code>AIC</code></dt>
<dd>object of class <code>"numeric"</code>,
Akaike information criterion.</dd>


<dt><code>LRTpvalue</code></dt>
<dd>object of class <code>"numeric"</code>,
likelihood ratio test p value.</dd>


<dt><code>gradient</code></dt>
<dd>object of class <code>"numeric"</code> or <code>"matrix"</code>,
containing the gradient.</dd>


<dt><code>iter</code></dt>
<dd>object of class <code>"numeric"</code>, 
number of iteration used.</dd>


<dt><code>distribution</code></dt>
<dd>object of class <code>"character"</code>,
the distribution fitted.</dd>


<dt><code>fitted</code></dt>
<dd>object of class <code>"vector"</code>,
the fitted mean of each category.</dd>


<dt><code>LRT</code></dt>
<dd>object of class <code>"numeric"</code>, 
the likelihood ratio test statistic.</dd>


</dl>

MGLMfit-class: Class `"MGLMfit"`

Description

Arguments

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Author

Examples