PRIDITweight {pridit} | R Documentation |
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)
[Package pridit version 1.1.0 Index]