summary.catpredi {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 class 'catpredi'
summary(object, digits = 4, ...)

Arguments

object

an object of class catpredi as produced by catpredi()

digits

.

...

further arguments passed to or from other methods.

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.

Author(s)

Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui

References

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

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