pred.mvt

It takes a fitted <code>gjrm</code> object produced 
by <code>gjrm()</code> and 
produces predictions and respective intervals.

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

pred.mvt function

<dl>
 
 
<dt>x</dt>
<dd>A fitted <code>gjrm</code> object.</dd>
<dt>eq</dt>
<dd>The equation number.</dd>
<dt>fun</dt>
<dd>Either mean, variance or tau.</dd>
<dt>n.sim</dt>
<dd>The number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used 
to calculate intervals. It may be increased if
 more precision is required.</dd>
 <dt>prob.lev</dt>
<dd>Probability of the left and right tails of the posterior distribution used for interval calculations.</dd>
 <dt>smooth.no</dt>
<dd>Smooth number if the interest is in a particular smooth and not the additive predictor(s).</dd>
 
<dt>...</dt>
<dd>Other parameters.</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 produced 
by <code>gjrm()</code> and 
produces predictions and respective intervals.

Function to predict mean and variance of marginal distributions, as well as Kendall's tau — pred.mvt

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


<dt>eq</dt>
<dd>The equation number.</dd>


<dt>fun</dt>
<dd>Either mean, variance or tau.</dd>


<dt>n.sim</dt>
<dd>The number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used 
to calculate intervals. It may be increased if
 more precision is required.</dd>
 

<dt>prob.lev</dt>
<dd>Probability of the left and right tails of the posterior distribution used for interval calculations.</dd>
 

<dt>smooth.no</dt>
<dd>Smooth number if the interest is in a particular smooth and not the additive predictor(s).</dd>
 


<dt>...</dt>
<dd>Other parameters.</dd>



</dl>

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

pred.mvt: Function to predict mean and variance of marginal distributions, as well as Kendall's tau

Description

Usage

Arguments

Author

See Also