specificity calculates the specificity and (optionally) the associated standard deviation from a confusion matrix.
● Data Source:
CranContrib
● Keywords: models
● Alias: specificity
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sensitivity calculates the sensitivity and (optionally) the associated standard deviation from a confusion matrix.
● Data Source:
CranContrib
● Keywords: models
● Alias: sensitivity
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roc.plot.calculate calculates PCC, sensitivity, specificity, and Kappa for a single presence absence model at a series of thresholds in preparation for creating a ROC plot.
● Data Source:
CranContrib
● Keywords: models
● Alias: roc.plot.calculate
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0 images
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This package provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
● Data Source:
CranContrib
● Keywords: package
● Alias: PresenceAbsence, PresenceAbsence-package
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Produces four types of Presence/Absence accuracy plots for a single set of model Predictions.
● Data Source:
CranContrib
● Keywords: models
● Alias: presence.absence.summary
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2 images
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presence.absence.simulation simulates presence/absence data as one set of observed values, and one or more prediction models. First, Observed values are generated as a binomial distribution, then for each model two beta distributions are used to generate predicted values, one beta distribution for the data points where the simulated observed value is present, and a second for points where it is absent.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: presence.absence.simulation
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1 images
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Produces a histogram of predicted probabilities with each bar subdivided by observed values. presence.absence.hist also includes an option to mark several types of optimal thresholds along each plot.
● Data Source:
CranContrib
● Keywords: models
● Alias: presence.absence.hist
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2 images
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Calculates five accuracy measures (pcc, sensitivity, specificity, Kappa, and AUC) for Presence/Absence data, and (optionally) their associated standard deviations.
● Data Source:
CranContrib
● Keywords: models
● Alias: presence.absence.accuracy
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predicted.prevalence calculates the observed prevalence and predicted prevalence for one or more models at one or more thresholds.
● Data Source:
CranContrib
● Keywords: models
● Alias: predicted.prevalence
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pcc
(Package: PresenceAbsence) :
Percent Correctly Classified
pcc calculates the percent correctly classified and (optionally) the associated standard deviation from a confusion matrix.
● Data Source:
CranContrib
● Keywords: models
● Alias: pcc
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providing 
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