predict.ldt.estim.varma

This function extracts predicted mean and its variance from an <code>ldt.estim.varma</code> object.
new data must be provided while estimating the model.

Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others).
'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
Evaluations are both in-sample and out-of-sample.
It is designed to be efficient in terms of CPU usage and memory consumption.

Ramin Mojab

Automated Uncertainty Analysis

Stephen Becker

predict.ldt.estim.varma function

<dl><dt>object</dt>
<dd>An object of class <code>ldt.estim.varma</code></dd>
<dt>actualCount</dt>
<dd>Number of actual observations to be included in the result.</dd>
<dt>startFrequency</dt>
<dd>Frequency of the first observation used in the estimation.
This is object of class <code>ldtf</code>.</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Extract Prediction Results from a ldt.estim.varma Object — predict.ldt.estim.varma

<dl>

<dt>object</dt>
<dd>An object of class <code>ldt.estim.varma</code></dd>


<dt>actualCount</dt>
<dd>Number of actual observations to be included in the result.</dd>


<dt>startFrequency</dt>
<dd>Frequency of the first observation used in the estimation.
This is object of class <code>ldtf</code>.</dd>


<dt>...</dt>
<dd>Additional arguments.</dd>

</dl>

predict.ldt.estim.varma: Extract Prediction Results from a `ldt.estim.varma` Object

Description

Usage

Value

Arguments

Details