| Title: | Computing False Positive Rate from Inter-Rater Reliability |
| Version: | 0.1.1 |
| Maintainer: | František Bartoš <f.bartos96@gmail.com> |
| Description: | Implements a 'Shiny Item Analysis' module and functions for computing false positive rate and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2024) <doi:10.1111/bmsp.12343>. |
| URL: | https://github.com/FBartos/IRR2FPR |
| BugReports: | https://github.com/FBartos/IRR2FPR/issues |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Config/ShinyItemAnalysis/module: | true |
| Imports: | shiny, mvtnorm |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2024-04-24 13:48:08 UTC; fbart |
| Author: | František Bartoš 
[aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-04-24 15:30:12 UTC |
Interactive Module for Inter-Rater Reliability to False Positive Rate Conversion
Description
This module allows users to convert inter-rater reliability (IRR) to false positive rate (FPR) as described in Bartoš and Martinková (2024).
Author(s)
František Bartoš
References
Bartoš, F., & Martinková, P. (2024). Selecting applicants based on multiple ratings: Using binary classification framework as an alternative to inter-rater reliability. British Journal of Mathematical and Statistical Psychology. doi:10.1111/bmsp.12343
IRR2FPR module (internal documentation)
Description
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Usage
IRR2FPR_ui(id, imports = NULL, ...)
IRR2FPR_server(id, imports = NULL, ...)
Arguments
id |
character, the ID assigned by ShinyItemAnalysis. Do not set any default value! |
imports |
list, reactive objects exported by ShinyItemAnalysis. See
|
... |
additional parameters (not used at the moment). |
Compute the false negative rate
Description
Compute the false negative rate based on the inter-rater reliability and the proportion of selected candidates
Usage
compute_false_negative_rate(IRR, proportion_selected)
Arguments
IRR |
The inter-rater reliability |
proportion_selected |
The proportion of selected candidates |
Value
The false negative rate
Examples
compute_false_negative_rate(0.75, 0.10)
Compute the false positive rate
Description
Compute the false positive rate based on the inter-rater reliability and the proportion of selected candidates
Usage
compute_false_positive_rate(IRR, proportion_selected)
Arguments
IRR |
The inter-rater reliability |
proportion_selected |
The proportion of selected candidates |
Value
The false positive rate
Examples
compute_false_positive_rate(0.75, 0.10)
Compute the proportion of correctly selected candidates
Description
Compute proportion of correctly selected candidates based on the inter-rater reliability and the proportion of selected candidates
Usage
compute_proportion_of_correctly_selected(IRR, proportion_selected)
Arguments
IRR |
The inter-rater reliability |
proportion_selected |
The proportion of selected candidates |
Value
The proportion of correctly selected candidates
Examples
compute_proportion_of_correctly_selected(0.75, 0.10)
Compute the true positive rate
Description
Compute the true positive rate based on the inter-rater reliability and the proportion of selected candidates
Usage
compute_true_positive_rate(IRR, proportion_selected)
Arguments
IRR |
The inter-rater reliability |
proportion_selected |
The proportion of selected candidates |
Value
The true positive rate
Examples
compute_true_positive_rate(0.75, 0.10)
Compute IRR from the Spearman-Brown formula
Description
Compute the inter-rater reliability based on the Spearman-Brown formula
Usage
spearman_brown_formula(IRR_1, n_raters)
Arguments
IRR_1 |
The inter-rater reliability of the first rater |
n_raters |
The number of raters |
Value
The inter-rater reliability
Examples
spearman_brown_formula(0.5, 3)