cutpointr_ {cutpointr} | R Documentation |

## The standard evaluation version of cutpointr (deprecated)

### Description

This function is equivalent to `cutpointr`

but takes only quoted arguments
for `x`

, `class`

and `subgroup`

. This was useful before
`cutpointr`

supported tidyeval.

### Usage

```
cutpointr_(
data,
x,
class,
subgroup = NULL,
method = maximize_metric,
metric = sum_sens_spec,
pos_class = NULL,
neg_class = NULL,
direction = NULL,
boot_runs = 0,
boot_stratify = FALSE,
use_midpoints = FALSE,
break_ties = median,
na.rm = FALSE,
allowParallel = FALSE,
silent = FALSE,
tol_metric = 1e-06,
...
)
```

### Arguments

`data` |
A data.frame with the data needed for x, class and optionally subgroup. |

`x` |
(character) The variable name to be used for classification, e.g. predictions or test values. |

`class` |
(character) The variable name indicating class membership. |

`subgroup` |
(character) The variable name of an additional covariate that identifies subgroups. Separate optimal cutpoints will be determined per group. |

`method` |
(function) A function for determining cutpoints. Can be user supplied or use some of the built in methods. See details. |

`metric` |
(function) The function for computing a metric when using maximize_metric or minimize_metric as method and and for the out-of-bag values during bootstrapping. A way of internally validating the performance. User defined functions can be supplied, see details. |

`pos_class` |
(optional) The value of class that indicates the positive class. |

`neg_class` |
(optional) The value of class that indicates the negative class. |

`direction` |
(character, optional) Use ">=" or "<=" to indicate whether x is supposed to be larger or smaller for the positive class. |

`boot_runs` |
(numerical) If positive, this number of bootstrap samples will be used to assess the variability and the out-of-sample performance. |

`boot_stratify` |
(logical) If the bootstrap is stratified, bootstrap samples are drawn separately in both classes and then combined, keeping the proportion of positives and negatives constant in every resample. |

`use_midpoints` |
(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">=") or the next lowest observation (for direction = "<=") which avoids biasing the optimal cutpoint. |

`break_ties` |
If multiple cutpoints are found, they can be summarized using this function, e.g. mean or median. To return all cutpoints use c as the function. |

`na.rm` |
(logical) Set to TRUE (default FALSE) to keep only complete cases of x, class and subgroup (if specified). Missing values with na.rm = FALSE will raise an error. |

`allowParallel` |
(logical) If TRUE, the bootstrapping will be parallelized using foreach. A local cluster, for example, should be started manually beforehand. |

`silent` |
(logical) If TRUE suppresses all messages. |

`tol_metric` |
All cutpoints will be returned that lead to a metric
value in the interval [m_max - tol_metric, m_max + tol_metric] where
m_max is the maximum achievable metric value. This can be used to return
multiple decent cutpoints and to avoid floating-point problems. Not supported
by all |

`...` |
Further optional arguments that will be passed to method. minimize_metric and maximize_metric pass ... to metric. |

### Examples

```
library(cutpointr)
## Optimal cutpoint for dsi
data(suicide)
opt_cut <- cutpointr_(suicide, "dsi", "suicide")
opt_cut
summary(opt_cut)
plot(opt_cut)
predict(opt_cut, newdata = data.frame(dsi = 0:5))
```

*cutpointr*version 1.1.2 Index]