ridit: Calculate the ridit values for a matrix

In pridit: Principal Component Analysis Applied to Ridit Scoring

Calculate the ridit values for a matrix

Description

This function takes a matrix of data and returns the matrix transformed as ridit values using the method developed by Bross (1958) and modified by Brockett et al. (2002).

Usage

ridit(allrawdata)

Arguments

allrawdata

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

Value

A data frame with the first column containing IDs (named "Claim.ID") and the remaining columns containing ridit scores for each variable.

References

Bross, I. D. (1958). How to use ridit analysis. Biometrics, 14(1), 18-38. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/2527727")}

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/1539-6975.00027")}

Examples

# Create sample data
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)
)

# Calculate ridit scores
ridit_result <- ridit(test_data)
print(ridit_result)

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