binaryCM: Confusion matrix summaries

In TalhoukLab/biostatUtil: Utility Functions Used in Biostatistics Projects

Confusion matrix summaries

Description

Calculates summaries from cross-tabulated reference and prediction labels for a two-class variable.

Usage

binaryCM(
  x,
  y,
  seed = 20,
  num.boot = 1000,
  pcond = 1,
  conf.level = 0.95,
  digits = 4,
  method = "wilson",
  verbose = FALSE
)

Arguments

x	a vector of reference classes
y	a vector of predicted classes
seed	random seed for bootstrapping
num.boot	number of times to bootstrap. Defaults to 1000.
pcond	a string or value to be considered positive condition; defaults to 1.
conf.level	confidence level. Defaults to 95%.
digits	number of digits to round summaries to
method	method for obtaining confidence intervals for binomial probabilities. See Hmisc::binconf for details.
verbose	logical; if TRUE, outputs are printed to the screen

Details

Given two dichotomous variables summarized in a confusion matrix, this function provides performance summaries. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the kappa statistic, along with their bootstrapped confidence intervals are returned.

Note that the classes given in x and y must be binary.

Value

A confusion matrix for the predicted and reference classes. Then the estimated statistics along with bootstrapped confidence intervals. A list with the following elements

CM	The confusion matrix, whose columns are the predicted conditions and its rows are the true conditions
Accuracy	Accuracy point estimate, lower bound and upper bound for bootstrapped CI
Sensitivity	Sensitivity point estimate, lower bound and upper bound for bootstrapped CI
Specificity	Specificity point estimate, lower bound and upper bound for bootstrapped CI
PPV	PPV point estimate, lower bound and upper bound for bootstrapped CI
NPV	NPV point estimate, lower bound and upper bound for bootstrapped CI
kappa	kappa point estimate, lower bound and upper bound for bootstrapped CI
table	a data frame that contains all 6 of the estimated statistics along with confidence intervals

Author(s)

Aline Talhouk, Derek Chiu

See Also

Other confusion matrix functions: binaryCMAsHTML(), multiClassCM()

Examples

### 95% CI from 1000 bootstraped samples
set.seed(547)
n <- 80
x <- rbinom(n, size = 1, prob = 0.6)
y <- rbinom(n, size = 1, prob = 0.4)
binaryCM(x, y)

### 90% CI from 500 bootstrapped samples
binaryCM(x, y, num.boot = 500, conf.level = 0.90)

### Round to 2 digits
binaryCM(x, y, digits = 2)

TalhoukLab/biostatUtil documentation built on April 14, 2025, 4:15 a.m.