sensitivity: Calculate the sensitivity for a given logit model
In glossa: User-Friendly 'shiny' App for Bayesian Species Distribution Models
sensitivity R Documentation
Calculate the sensitivity for a given logit model
Description
Calculate the sensitivity for a given logit model
Usage
sensitivity(actuals, predictedScores, threshold = 0.5)
Arguments
actuals
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'.
predictedScores
The prediction probability scores for each observation. If your classification model gives the 1/0 predictions, convert it to a numeric vector of 1's and 0's.
threshold
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.
Details
This function was obtained from the InformationValue R package (https://github.com/selva86/InformationValue).
Value
The sensitivity of the given binary response actuals and predicted probability scores, which is, the number of observations with the event AND predicted to have the event divided by the number of observations with the event.
glossa documentation built on June 8, 2025, 1:20 p.m.
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| sensitivity | R Documentation |
Calculate the sensitivity for a given logit model
Description
Calculate the sensitivity for a given logit model
Usage
sensitivity(actuals, predictedScores, threshold = 0.5)
Arguments
actuals |
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'. |
predictedScores |
The prediction probability scores for each observation. If your classification model gives the 1/0 predictions, convert it to a numeric vector of 1's and 0's. |
threshold |
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. |
Details
This function was obtained from the InformationValue R package (https://github.com/selva86/InformationValue).
Value
The sensitivity of the given binary response actuals and predicted probability scores, which is, the number of observations with the event AND predicted to have the event divided by the number of observations with the event.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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