caStats: Classification Accuracy Statistics.

In betafunctions: Functions for Working with Two- And Four-Parameter Beta Probability Distributions and Psychometric Analysis of Classifications

Classification Accuracy Statistics.

Description

Provides a set of statistics often used for conveying information regarding the certainty of classifications based on tests.

Usage

caStats(tp, tn, fp, fn)

Arguments

tp	The frequency or rate of true-positive classifications.
tn	The frequency or rate of true-negative classifications.
fp	The frequency or rate of false-positive classifications.
fn	The frequency or rate of false-negative classifications.

Value

A list of diagnostic performance statistics based on true/false positive/negative statistics. Specifically, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Youden's J. (Youden.J), and Accuracy.

Examples

# Generate some fictional data. Say, 1000 individuals take a test with a
# maximum score of 100 and a minimum score of 0.
set.seed(1234)
testdata <- rbinom(1000, 100, rBeta.4P(1000, 0.25, 0.75, 5, 3))
hist(testdata, xlim = c(0, 100))

# Suppose the cutoff value for attaining a pass is 50 items correct, and
# that the reliability of this test was estimated to 0.7. First, compute the
# estimated confusion matrix using LL.CA():
cmat <- LL.CA(x = testdata, reliability = 0.7, cut = 50, min = 0,
max = 100)$confusionmatrix

# To estimate and retrieve diagnostic performance statistics using caStats(),
# feed it the appropriate entries of the confusion matrix.
caStats(tp = cmat["True", "Positive"], tn = cmat["True", "Negative"],
fp = cmat["False", "Positive"], fn = cmat["False", "Negative"])

betafunctions documentation built on May 29, 2024, 1:13 a.m.