plot.catpredi.survival: Plot the optimal cut points.

In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

Description

Plots the functional form of the predictor variable we want to categorise. Additionally, the optimal cut points obtained with the catpredi.survival() function are drawn on the graph.

Usage

## S3 method for class 'catpredi.survival'
plot(x, ...)

Arguments

x	An object of type catpredi.survival .
...	Additional arguments to be passed on to other functions. Not yet implemented.

Value

This function returns the plot of the relationship between the predictor variable and the outcome.

Author(s)

Irantzu Barrio and Maria Xose Rodriguez-Alvarez

References

I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92

See Also

See Also as catpredi.survival.

Examples

library(CatPredi)
library(survival)
set.seed(123)
#Simulate data
  n = 500
  tauc = 1
  X <- rnorm(n=n, mean=0, sd=2)
  SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1))   + rnorm(n, mean=0, sd=0.25)
  # Censoring time
  CensTime <- runif(n=n, min=0, max=tauc)
  # Status
  SurvS <- as.numeric(SurvT <= CensTime)
  # Data frame
  dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS)
 
  # Select optimal cut points using the AddFor algorithm
  res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, 
  data = dat, method = "addfor", conc.index = "cindex", range = NULL, 
  correct.index = FALSE) 
  # Plot
  plot(res)

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