CatPredi (version 1.4)

plot.catpredi: Plot the optimal cut points.

Description

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.

Usage

# S3 method for catpredi
plot(x, ...)

Value

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

Arguments

x

An object of type catpredi.

...

Additional arguments to be passed on to other functions. Not yet implemented.

Author

Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui

References

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.

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.

See Also

See Also as catpredi.

Examples

  library(CatPredi)
  # \donttest{
  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.backaddfor <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, 
  				data = df, method = "backaddfor", range = NULL, correct.AUC = FALSE)
  # Plot
  plot(res.backaddfor)
  # }