summary.ldt.search

Use this function to get the full estimation of the models reported in the output of a search process.

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

summary.ldt.search function

<dl><dt>object</dt>
<dd>An <code>ldt.search</code> object.</dd>
<dt>test</dt>
<dd>If <code>TRUE</code> and applicable (e.g., in model estimation), it checks the metrics and throws error for any inconsistencies between the current estimation and the one calculated in the search process (Provided that negative seed is used).</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Summary for an ldt.search object — summary.ldt.search

<dl>

<dt>object</dt>
<dd>An <code>ldt.search</code> object.</dd>


<dt>test</dt>
<dd>If <code>TRUE</code> and applicable (e.g., in model estimation), it checks the metrics and throws error for any inconsistencies between the current estimation and the one calculated in the search process (Provided that negative seed is used).</dd>


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

</dl>

summary.ldt.search: Summary for an `ldt.search` object

Description

Usage

Value

Arguments

Details