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

Returns an object with the optimal cut points to categorise a continuous predictor variable in a logistic regression model

1 2 3 |

`formula` |
An object of class |

`cat.var` |
Name of the continuous variable to categorise. |

`cat.points` |
Number of cut points to look for. |

`data` |
Data frame containing all needed variables. |

`method` |
The algorithm selected to search for the optimal cut points. "addfor" if the AddFor algorithm is choosen and "genetic" otherwise. |

`range` |
The range of the continuous variable in which to look for the cut points. By default NULL, i.e, all the range. |

`correct.AUC` |
A logical value. If TRUE the bias corrected AUC is estimated. |

`control` |
Output of the controlcatpredi() function. |

`...` |
Further arguments for passing on to the function |

Returns an object of class "catpredi" with the following components:

`call` |
the matched call. |

`method` |
the algorithm selected in the call. |

`formula` |
an object of class |

`cat.var` |
name of the continuous variable to categorise. |

`data` |
the data frame with the variables used in the call. |

`correct.AUC` |
The logical value used in the call. |

`results` |
a list with the estimated cut points, AUC and bias corrected AUC. |

`control` |
the control parameters used in the call. |

For each of the methods used in the call, a list with the following components is obtained:

`"cutpoints"` |
Estimated optimal cut points. |

`"AUC"` |
Estimated AUC. |

`"AUC.cor"` |
Estimated bias corrected AUC. |

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).

S.N Wood (2006). Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.

See Also as `controlcatpredi`

, `comp.cutpoints`

, `plot.catpredi`

,
`summary.catpredi`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
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 = 3,
data = df, method = "addfor", range=NULL, correct.AUC=FALSE)
``` |

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