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

In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

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"), range = NULL, 
correct.AUC = TRUE, 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 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	an object of class formula giving the model to be fitted in addition to the continuous covariate is aimed to categorise.
cat.var	name of the continuous variable to categorise.
data	the data frame with the variables used in the call.
correct.AUC	The logical value used in the call.
results	a list with the estimated cut points, AUC and bias corrected AUC.
control	the control parameters used in the call.

For each of the methods used in the call, a list with the following components is obtained:

"cutpoints"	Estimated optimal cut points.
"AUC"	Estimated AUC.
"AUC.cor"	Estimated bias corrected AUC.

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).

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

See Also

See Also as controlcatpredi, comp.cutpoints, plot.catpredi, summary.catpredi.

Examples

library(CatPredi)


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)
 

CatPredi documentation built on Jan. 10, 2022, 5:07 p.m.