getFprTpr

Compute specificity and sensitivity

internal

A user-friendly 'shiny' application for Bayesian machine
learning analysis of marine species distributions. GLOSSA (Global
Species Spatiotemporal Analysis) uses Bayesian Additive Regression
Trees (BART; Chipman, George, and McCulloch (2010)
<doi:10.1214/09-AOAS285>) to model species distributions with
intuitive workflows for data upload, processing, model fitting, and
result visualization. It supports presence-absence and presence-only
data (with pseudo-absence generation), spatial thinning,
cross-validation, and scenario-based projections. GLOSSA is designed
to facilitate ecological research by providing easy-to-use tools for
analyzing and visualizing marine species distributions across
different spatial and temporal scales.

Jorge Mestre-Tomás

glossa

User-Friendly 'shiny' App for Bayesian Species Distribution
Models

Alba Fuster-Alonso

getFprTpr function

<dl><dt>actuals</dt>
<dd>The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.</dd>
<dt>predictedScores</dt>
<dd>The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's.</dd>
<dt>threshold</dt>
<dd>If predicted value is above the threshold, it will be considered as an event (1), else it will be a non-event (0). Defaults to 0.5.</dd></dl>

Arguments

Compute specificity and sensitivity — getFprTpr

<dl>

<dt>actuals</dt>
<dd>The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.</dd>


<dt>predictedScores</dt>
<dd>The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's.</dd>


<dt>threshold</dt>
<dd>If predicted value is above the threshold, it will be considered as an event (1), else it will be a non-event (0). Defaults to 0.5.</dd>

</dl>

getFprTpr: Compute specificity and sensitivity

Description

Usage

Value

Arguments

Details