CatPredi (version 1.4)

comp.cutpoints: Selection of optimal number of cut points

Description

Compares two objects of class "catpredi".

Usage

comp.cutpoints(obj1, obj2, V = 100)

Value

This function returns an object of class "comp.cutpoints" with the following components:

AUC.cor.diff

the difference of the bias corrected AUCs for the two categorical variables.

icb.auc.diff

bootstrap based confidence interval for the bias corrected AUC difference.

Arguments

obj1

an object inheriting from class "catpredi" for k number of cut points

obj2

an object inheriting from class "catpredi" for k+1 number of cut points

V

Number of bootstrap resamples. By default V=100

Author

Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui

References

I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2017). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research, 26(6), 2586-2602.

See Also

See Also as catpredi.

Examples

library(CatPredi)
set.seed(127)
#Simulate data
  n = 100
  #Predictor variable
  xh <- rnorm(n, mean = 0, sd = 1)
  xd <- rnorm(n, mean = 1.5, sd = 1)
  x <- c(xh, xd)
  #Response
  y <- c(rep(0,n), rep(1,n))
  # Data frame
  df <- data.frame(y = y, x = x)
# \dontshow{   
  # Select 2 optimal cut points using the AddFor algorithm. Correct the AUC
  res.backaddfor.k2 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 2, 
  data = df, method = "backaddfor", range=NULL, correct.AUC=TRUE, 
  control=controlcatpredi(grid=20))
  # Select 3 optimal cut points using the AddFor algorithm. Correct the AUC
  res.backaddfor.k3 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, 
  data = df, method = "backaddfor", range=NULL, correct.AUC=TRUE, 
  control=controlcatpredi(grid=20))
  comp <-  comp.cutpoints(res.backaddfor.k2, res.backaddfor.k3, V = 10)
# }
# \donttest{ 
  # Select 2 optimal cut points using the AddFor algorithm. Correct the AUC
  res.backaddfor.k2 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 2, 
  data = df, method = "backaddfor", range=NULL, correct.AUC=TRUE, 
  control=controlcatpredi(grid=100))
  # Select 3 optimal cut points using the AddFor algorithm. Correct the AUC
  res.backaddfor.k3 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, 
  data = df, method = "backaddfor", range=NULL, correct.AUC=TRUE, 
  control=controlcatpredi(grid=100))     
  # Select optimal number of cut points
  comp <-  comp.cutpoints(res.backaddfor.k2, res.backaddfor.k3, V = 100)
# }