Compute Concordance Measures

Computes pairwise concordance statistics between different algorithms for probable Parkinson’s disease dementia (PDD). Metrics include Cohen's Kappa, Sensitivity, Positive Predictive Value (PPV), Specificity, Negative Predictive Value (NPV), and Accuracy. Visual summaries are also generated via ggplot2.

Usage

describe_concordance(d0)

Arguments

d0

A list containing PDD diagnostic results, as produced by diagnose_pdd_sample.

Value

A list with two components:

table

A data frame summarising pairwise concordance statistics (e.g., Kappa, Accuracy, Sensitivity, Specificity, PPV, NPV). The table is propagated with the package as data concords

.

plots

A list of ggplot2-based visualisations, including matrices of Cohen's Kappa, Accuracy (with significance tests against the No Information Rate), Sensitivity, and Specificity.

See also

• diagnose_pdd_sample() prepares d0.

• concords contains the table of results generated by this function.

• psych::cohen.kappa() computes and documents Cohen’s kappa and related metrics.

• caret::confusionMatrix() computes and documents the remaining metrics.

Examples

if (FALSE) { # \dontrun{
p <- data_paths("data-raw")
data <- prepare_data(p)
pdd <- diagnose_pdd_sample(data)
concmats <- describe_concordance(pdd)
} # }