multi_cutpointr {cutpointr} | R Documentation |

## Calculate optimal cutpoints and further statistics for multiple predictors

### Description

Runs `cutpointr`

over multiple predictor variables. Tidyeval via
`!!`

is supported for `class`

and `subgroup`

. If
`x = NULL`

, `cutpointr`

will be run using all numeric columns
in the data set as predictors except for the
variable in `class`

and, if given, `subgroup`

.

### Usage

```
multi_cutpointr(data, x = NULL, class, subgroup = NULL, silent = FALSE, ...)
```

### Arguments

`data` |
A data frame. |

`x` |
Character vector of predictor variables. If NULL all numeric columns. |

`class` |
The name of the outcome / independent variable. |

`subgroup` |
An additional covariate that identifies subgroups. Separate optimal cutpoints will be determined per group. |

`silent` |
Whether to suppress messages. |

`...` |
Further arguments to be passed to cutpointr, e.g., boot_runs |

### Details

The automatic determination of positive / negative classes and `direction`

will be carried out separately for every predictor variable. That way, if
`direction`

and the classes are not specified, the reported AUC for every
variable will be >= 0.5. AUC may be < 0.5 if subgroups are specified as
`direction`

is equal within every subgroup.

### Value

A data frame.

### See Also

Other main cutpointr functions:
`add_metric()`

,
`boot_ci()`

,
`boot_test()`

,
`cutpointr()`

,
`predict.cutpointr()`

,
`roc()`

### Examples

```
library(cutpointr)
multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide,
pos_class = "yes")
mcp <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide,
subgroup = gender, pos_class = "yes")
mcp
(scp <- summary(mcp))
## Not run:
## The result is a data frame
tibble:::print.tbl(scp)
## End(Not run)
```

*cutpointr*version 1.1.2 Index]