consolidate_partial_results

Consolidates results from a set of partial files (each generated by an
individual call to <code>calc_nreps()</code>) into a single output structure, similar
(but not identical) to the output of <code>run_experiment()</code>. This is useful
e.g., to consolidate the results from interrupted experiments.

Functions for performing experimental comparisons of algorithms
using adequate sample sizes for power and accuracy. Implements the
methodology originally presented in Campelo and Takahashi (2019)
<doi:10.1007/s10732-018-9396-7>
for the comparison of two algorithms, and later generalised in
Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

Felipe Campelo

CAISEr

Comparison of Algorithms with Iterative Sample Size Estimation

Fernanda Takahashi

Elizabeth Wanner

consolidate_partial_results function

<dl><dt>Configuration</dt>
<dd>a named list containing all parameters
required in a call to <code>run_experiment()</code> except
<code>instances</code> and <code>algorithms</code>. See the parameter list
and default values in <code>run_experiment()</code>. Notice that
this is always returned as part of the output structure
of <code>run_experiment()</code>, so it generally easier to just
retrieve it from previously saved results.</dd>
<dt>folder</dt>
<dd>folder where the partial files are located.</dd></dl>

Arguments

Consolidate results from partial files — consolidate_partial_results

<dl>

<dt>Configuration</dt>
<dd>a named list containing all parameters
required in a call to <code>run_experiment()</code> except
<code>instances</code> and <code>algorithms</code>. See the parameter list
and default values in <code>run_experiment()</code>. Notice that
this is always returned as part of the output structure
of <code>run_experiment()</code>, so it generally easier to just
retrieve it from previously saved results.</dd>


<dt>folder</dt>
<dd>folder where the partial files are located.</dd>

</dl>

consolidate_partial_results: Consolidate results from partial files

Description

Usage

Value

Arguments