MAP.discrete

This function computes the estimation of a latent variables foe=r each cluster using the conditional a posteriori median.

Estimation of 2-level factor copula-based regression models  for clustered data where the response variable can be either discrete or continuous.

Bruno Remillard

CopulaGAMM

Copula-Based Mixed Regression Models

Pavel Krupskii

MAP.discrete function

<dl><dt>vv</dt>
<dd>vector of values in (0,1)</dd>
<dt>uu</dt>
<dd>vector of values in (0,1)</dd>
<dt>family</dt>
<dd>copula family "gaussian" , "t" , "clayton" , "joe", "frank" , "fgm", gumbel", "plackett", "galambos", "huesler-reiss"</dd>
<dt>rot</dt>
<dd>rotation: 0 (default), 90, 180 (survival), or 270.</dd>
<dt>thC0k</dt>
<dd>vector of copula parameters</dd>
<dt>dfC</dt>
<dd>degrees of freedom for the Student copula (default is NULL)</dd>
<dt>nq</dt>
<dd>number of nodes and weighted for Gaussian quadrature of the product of conditional copulas; default is 31.</dd></dl>

Arguments

Pavel Krupskii, Bouchra R. Nasri and Bruno N. Remillard

Author

Estimation of latent variable in the dicrete case — MAP.discrete

<dl>

<dt>vv</dt>
<dd>vector of values in (0,1)</dd>


<dt>uu</dt>
<dd>vector of values in (0,1)</dd>


<dt>family</dt>
<dd>copula family "gaussian" , "t" , "clayton" , "joe", "frank" , "fgm", gumbel", "plackett", "galambos", "huesler-reiss"</dd>


<dt>rot</dt>
<dd>rotation: 0 (default), 90, 180 (survival), or 270.</dd>


<dt>thC0k</dt>
<dd>vector of copula parameters</dd>


<dt>dfC</dt>
<dd>degrees of freedom for the Student copula (default is NULL)</dd>


<dt>nq</dt>
<dd>number of nodes and weighted for Gaussian quadrature of the product of conditional copulas; default is 31.</dd>

</dl>

MAP.discrete: Estimation of latent variable in the dicrete case

Description

Usage

Value

Arguments

Author

References

Examples