miceCompare

Statistically compare two models using a paired t-test and bootstrap samples of the assessment results

Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021)<doi:10.1109/ACCESS.2021.3116526> and Tang et al. (2024)<doi:10.1109/TGRS.2024.3446950>). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results.

Aaron Maxwell

micer

Map Image Classification Efficacy

Sarah Farhadpour

miceCompare function

<dl><dt>ref</dt>
<dd>column of reference labels as factor data type.</dd>
<dt>result1</dt>
<dd>column of predicted labels as factor data type (first result to compare).</dd>
<dt>result2</dt>
<dd>column of predicted labels as factor data type (second result to compare).</dd>
<dt>reps</dt>
<dd>number of bootstrap replicates to use. Default is 200.</dd></dl>

Arguments

miceCompare — miceCompare

<dl>

<dt>ref</dt>
<dd>column of reference labels as factor data type.</dd>


<dt>result1</dt>
<dd>column of predicted labels as factor data type (first result to compare).</dd>


<dt>result2</dt>
<dd>column of predicted labels as factor data type (second result to compare).</dd>


<dt>reps</dt>
<dd>number of bootstrap replicates to use. Default is 200.</dd>

</dl>

miceCompare: miceCompare

Description

Usage

Value

Arguments

Examples