PRIDITweight: Calculate the PRIDIT weights for a ridit matrix

In pridit: Principal Component Analysis Applied to Ridit Scoring

Calculate the PRIDIT weights for a ridit matrix

Description

This function takes a ridit-scored matrix and returns PRIDIT weights for each variable as a vector using Principal Component Analysis.

Usage

PRIDITweight(riditscores)

Arguments

riditscores

A data frame where the first column represents IDs. The IDs uniquely identify each row in the matrix. The remaining columns contain the ridit scores for each ID.

Value

A numeric vector containing PRIDIT weights for each variable.

References

Brockett, P. L., Derrig, R. A., Golden, L. L., Levine, A., & Alpert, M. (2002). Fraud classification using principal component analysis of RIDITs. Journal of Risk and Insurance, 69(3), 341-371.

Examples

# Create sample data and calculate ridit scores first
test_data <- data.frame(
  ID = c("A", "B", "C", "D", "E"),
  var1 = c(0.9, 0.85, 0.89, 1.0, 0.89),
  var2 = c(0.99, 0.92, 0.90, 1.0, 0.93),
  var3 = c(1.0, 0.99, 0.98, 1.0, 0.99)
)

# First calculate ridit scores
ridit_result <- ridit(test_data)

# Then calculate PRIDIT weights
weights <- PRIDITweight(ridit_result)
print(weights)

pridit documentation built on Aug. 8, 2025, 6:20 p.m.