plot.catpredi: Plot the optimal cut points.
In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

Description Usage Arguments Value Author(s) References See Also Examples

Plots the relationship between the predictor variable is aimed to categorise and the response variable based on a GAM model. Additionally, the optimal cut points obtained with the catpredi() function are drawn on the graph.

1 2	## S3 method for class 'catpredi' plot(x, ...)

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

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

Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui

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.

  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 optimal cut points using the AddFor algorithm
  res.addfor <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, 
  				data = df, method = "addfor", range = NULL, correct.AUC = FALSE)
  # Plot
  plot(res.addfor)