caStats

0th

Percentile

Classification Accuracy Statistics.

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 likelihood ratio (LR.pos), negative likelihood ratio (LR.neg), positive predictive value (PPV), negative predictive value (NPV), Youden's J. (Youden.J), and Accuracy.

References

Glas et al. (2003). The Diagnostic Odds Ratio: A Single Indicator of Test Performance, Journal of Clinical Epidemiology, 1129-1135, 56(11). doi: 10.1016/S0895-4356(03)00177-X

Aliases
  • caStats
Examples
# NOT RUN {
# Generate some fictional data. Say, 100 individuals take a test with a
# maximum score of 100 and a minimum score of 0.
set.seed(1234)
testdata <- rbinom(100, 100, rBeta.4P(100, .25, .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 = .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", "Fail"], tn = cmat["True", "Pass"],
fp = cmat["False", "Fail"], fn = cmat["False", "Pass"])
# }
Documentation reproduced from package betafunctions, version 1.2.2, License: CC0

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