comp.cutpoints: Selection of optimal number of cut points In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

Description

Compares two objects of class "catpredi".

Usage

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

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

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.

Author(s)

Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui

References

I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2015). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research (in press).

See Also as `catpredi`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```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) # Select 2 optimal cut points using the AddFor algorithm. Correct the AUC res.addfor.k2 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 2, data = df, method = "addfor", range=NULL, correct.AUC=TRUE, control=controlcatpredi(addfor.g=100)) # Select 3 optimal cut points using the AddFor algorithm. Correct the AUC res.addfor.k3 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, data = df, method = "addfor", range=NULL, correct.AUC=TRUE, control=controlcatpredi(addfor.g=100)) # Select optimal number of cut points comp <- comp.cutpoints(res.addfor.k2, res.addfor.k3, V = 100) ```