CatPredi (version 1.4)

summary.catpredi: Summary method for catpredi objects

Description

Produces a summary of a catpredi object. The following are printed: the call to the catpredi() function; the estimated optimal cut points obtained with the method selected and the estimated AUC and bias corrected AUC (if the argument correct.AUC is TRUE) for the categorised variable.

Usage

# S3 method for catpredi
summary(object, digits = 4, ...)

Value

Returns an object of class "summary.catpredi" with the same components as the catpredi function (see catpredi). plus:

fit.gam

fitted model according to the model specified in the call, based on the function gam of the package mgcv.

Arguments

object

an object of class catpredi as produced by catpredi()

digits

.

...

further arguments passed to or from other methods.

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)
 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.backaddfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 2, 
  data = df, method = "backaddfor", range=NULL, correct.AUC=FALSE)
  # Summary
  summary(res.backaddfor)