dx_mcc: Calculate Matthews Correlation Coefficient (MCC)

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate Matthews Correlation Coefficient (MCC)

Description

Calculates the Matthews Correlation Coefficient (MCC), a measure of the quality of binary classifications. It returns a value between -1 and +1 where +1 indicates perfect prediction, 0 no better than random prediction, and -1 indicates total disagreement between prediction and observation. The function can also return a confidence interval for the MCC value using bootstrapping if detail is set to "full".

Usage

dx_mcc(cm, detail = "full", boot = FALSE, bootreps = 1000)

Arguments

cm	A dx_cm object created by dx_cm().
detail	Character specifying the level of detail in the output: "simple" for raw estimate, "full" for detailed estimate including 95% confidence intervals.
boot	Logical specifying if confidence intervals should be generated via bootstrapping. Note, this can be slow.
bootreps	The number of bootstrap replications for calculating confidence intervals.

Details

The Matthews Correlation Coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The formula for MCC is:

MCC = \frac{(TP \times TN) - (FP \times FN)}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}}

where TP, TN, FP, and FN represent the counts of true positives, true negatives, false positives, and false negatives, respectively.

For "full" details, bootstrap methods are used to estimate the confidence interval for the MCC value, providing a more robust understanding of its stability.

Value

If detail is "simple", returns a single numeric value of MCC. If detail is "full", returns a data frame that includes MCC, its bootstrapped confidence interval, and other key details

Examples

cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
  threshold =
    0.5, poslabel = 1
)
mcc_simple <- dx_mcc(cm, detail = "simple")
mcc_full <- dx_mcc(cm)
print(mcc_simple)
print(mcc_full)

overdodactyl/diagnosticSummary documentation built on Jan. 28, 2024, 10:07 a.m.