pridit (version 1.1.0)

PRIDITweight: 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)

Value

A numeric vector containing PRIDIT weights for each variable.

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.

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)