summary.LRCpred: Summarize predictions of logistic regression classifier

In pnnl/glmnetLRC: Lasso and Elastic-Net Logistic Regression Classification with an Arbitrary Loss Function

Description

Summarize the predicted probabilities of the classifier, and, if possible, calculate accuracy, sensitivity, specificity, false positive rate, and false negative rate.

Usage

## S3 method for class 'LRCpred'
summary(object, ...)

## S3 method for class 'summaryLRCpred'
print(x, ...)

Arguments

object	An object of class LRCpred returned by predict.glmnetLRC.
x	An object of class summaryLRCpred.
...	Arguments passed to print methods. Ignored by the summary method.

Value

Returns a summaryLRCpred object. If truthCol was provided in the call to predict.glmnetLRC, the result is a list with the following elements:

ConfusionMatrixMetrics

A matrix with the sensitivity, specificity, false negative rate, false positive rate, and accuracy for the class designated by the second level of the truthLabels argument provided to glmnetLRC

PredProbSummary

A numeric summary of the predicted probabilities, according to the true class

If truthCol was not provided in the call to predict.glmnetLRC, the result is a list with the following elements:

PredClassSummary

A tabulation of the number of predictions in each class

PredProbSummary

A numeric summary of the predicted probabilities, according to the predicted class

Methods (by generic)

• print: Prints a summaryLRCpred object in a readable format.

Author(s)

Landon Sego

See Also

See glmnetLRC for examples.

pnnl/glmnetLRC documentation built on May 25, 2019, 10:22 a.m.