plot.catpredi: Plot the optimal cut points.

In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

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 class 'catpredi'
plot(x, ...)

Arguments

x	An object of type catpredi.
...	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, 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

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))
 # 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)
 
## End(Not run)

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