predict.CopulaCLM

It takes a fitted <code>gjrm</code> object for the ordinal-continuous case and, 
for each equation, produces predictions 
for a new set of values of the model covariates or the original values used for the model fit. 
Standard errors of predictions can be produced and are based on the posterior distribution of the model coefficients.

prediction

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.

Giampiero Marra

GJRM

Generalised Joint Regression Modelling

predict.CopulaCLM function

<dl>
 <dt>object</dt>
<dd>A fitted <code>gjrm</code> object.</dd>
<dt>eq</dt>
<dd>The equation to be considered for prediction.</dd>
<dt>type</dt>
<dd>Type of prediction.</dd>
<dt>...</dt>
<dd>Other arguments as in <code>predict.gam()</code> in <code>mgcv</code>.</dd>
</dl>

Arguments

Maintainer: Giampiero Marra <a href="/link/giampiero.marra%40ucl.ac.uk?package=GJRM&version=0.2-6.5" data-mini-rdoc="GJRM::giampiero.marra@ucl.ac.uk">giampiero.marra@ucl.ac.uk</a>

Author

It takes a fitted <code>gjrm</code> object for the ordinal-continuous case and, 
for each equation, produces predictions 
for a new set of values of the model covariates or the original values used for the model fit. 
Standard errors of predictions can be produced and are based on the posterior distribution of the model coefficients.

Prediction function — predict.CopulaCLM

<dl>
 

<dt>object</dt>
<dd>A fitted <code>gjrm</code> object.</dd>


<dt>eq</dt>
<dd>The equation to be considered for prediction.</dd>


<dt>type</dt>
<dd>Type of prediction.</dd>


<dt>...</dt>
<dd>Other arguments as in <code>predict.gam()</code> in <code>mgcv</code>.</dd>


</dl>

Maintainer: Giampiero Marra <a href='mailto:giampiero.marra@ucl.ac.uk'>giampiero.marra@ucl.ac.uk</a>

predict.CopulaCLM: Prediction function

Description

Usage

Arguments

Author

See Also