plr.cmatrix.md

plr.cmatrix	R Documentation

Positive Likelihood Ratio

Description

A generic S3 function to compute the positive likelihood ratio score for a classification model. This function dispatches to S3 methods inplr() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because plr() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap plr() in a "safe" validator that checks for NA values and matching length, for example:

safe_plr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  plr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

For multiple performance evaluations of a classification model, first compute the confusion matrix once via cmatrix(). All other performance metrics can then be derived from this one object via S3 dispatching:

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate positive likelihood ratio
## via S3 dispatching
plr(confusion_matrix)

## additional performance metrics
## below

The plr.factor() method calls cmatrix() internally, so explicitly invoking plr.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## S3 method for class 'cmatrix'
plr(x, ...)

Arguments

x	A confusion matrix created cmatrix().
...	Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
actual    = actual_classes,
predicted = predicted_classes
)

## Evaluate performance
SLmetrics::plr(confusion_matrix)

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