catpredi: Function to obtain optimal cut points to categorise a...

In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

Function to obtain optimal cut points to categorise a continuous predictor variable in a logistic regression model

Description

Returns an object with the optimal cut points to categorise a continuous predictor variable in a logistic regression model

Usage

catpredi(
  formula,
  cat.var,
  cat.points = 1,
  data,
  method = c("addfor", "genetic", "backaddfor"),
  range = NULL,
  correct.AUC = FALSE,
  control = controlcatpredi(),
  ...
)

Arguments

formula	An object of class formula giving the model to be fitted in addition to the continuous covariate is aimed to categorise. This argument allows the user to specify whether the continuous predictor should be categorised in a univariable context, or in presence of other covariates or cofounders, i.e in a multiple logistic regression model. For instance, Y ~ 1 indicates that the categorisation should be done in a univariable setting, with Y being the response variable. If the predictor variable is aimed to be categorised in a multivariable setting, this argument allows to specify whether the covariates should be modelled using linear or non linear effects. In the latest, the effects are estimated using the mgcv package.
cat.var	Name of the continuous variable to categorise.
cat.points	Number of cut points to look for.
data	Data frame containing all needed variables.
method	The algorithm selected to search for the optimal cut points. "addfor" if the AddFor algorithm is choosen, "backaddfor" if the BackAddFor algorithm is selected and "genetic" otherwise.
range	The range of the continuous variable in which to look for the cut points. By default NULL, i.e, all the range.
correct.AUC	A logical value. If TRUE the bias corrected AUC is estimated.
control	Output of the controlcatpredi function.
...	Further arguments for passing on to the function genoud of the package rgenoud.

Value

Returns an object of class "catpredi" with the following components:

call

The matched call.

method

The algorithm selected in the call.

formula

The model formula used in the call.

cat.var

Name of the continuous variable to categorise.

data

The data frame used in the call.

correct.AUC

Logical value indicating whether bias-corrected AUC was used.

results

A list containing estimated cut points, AUC and bias-corrected AUC for each method.

control

The control parameters used in the call.

Author(s)

Irantzu Barrio, Maria Xose Rodriguez-Alvarez, Inmaculada Arostegui, Javier Roca-Pardinas and Xabier Amutxastegi.

References

I Barrio, J Roca-Pardinas and I Arostegui (2021). Selecting the number of categories of the lymph node ratio in cancer research: A bootstrap-based hypothesis test. Statistical Methods in Medical Research, 30(3), 926-940.

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.

S.N Wood (2006). Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.

See Also

controlcatpredi, comp.cutpoints, plot.catpredi, summary.catpredi

Examples

library(CatPredi)
## Not run: 
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))
#Covariate
zh <- rnorm(n, mean=1.5, sd=1)
zd <- rnorm(n, mean=1, sd=1)
z <- c(zh, zd)
# Data frame
df <- data.frame(y = y, x = x, z = z)

# Select optimal cut points using the AddFor algorithm
res.addfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 2,
                       data = df, method = "addfor", range=NULL, correct.AUC=FALSE,
                       control=controlcatpredi(grid=20))

# Select optimal cut points using the BackAddFor algorithm
res.backaddfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 3,
                           data = df, method = "backaddfor", range=NULL, correct.AUC=FALSE)

## End(Not run)
## Not run: 
  set.seed(127)
  #Simulate data
  n = 200
  #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))
  #Covariate
  zh <- rnorm(n, mean=1.5, sd=1)
  zd <- rnorm(n, mean=1, sd=1)
  z <- c(zh, zd)
  # Data frame
  df <- data.frame(y = y, x = x, z = z)

  # Select optimal cut points using the AddFor algorithm
  res.addfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 3,
                         data = df, method = "addfor", range=NULL, correct.AUC=FALSE)

  # Select optimal cut points using the BackAddFor algorithm
  res.backaddfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 3,
                             data = df, method = "backaddfor", range=NULL, correct.AUC=FALSE)

## End(Not run)

CatPredi documentation built on May 8, 2026, 9:07 a.m.