maximize_metric {cutpointr}  R Documentation 
Optimize a metric function in binary classification
Description
Given a function for computing a metric in metric_func
, these functions
maximize or minimize that metric by selecting an optimal cutpoint.
The metric function should accept the following inputs:

tp
: vector of number of true positives 
fp
: vector of number of false positives 
tn
: vector of number of true negatives 
fn
: vector of number of false negatives
Usage
maximize_metric(
data,
x,
class,
metric_func = youden,
pos_class = NULL,
neg_class = NULL,
direction,
tol_metric,
use_midpoints,
...
)
minimize_metric(
data,
x,
class,
metric_func = youden,
pos_class = NULL,
neg_class = NULL,
direction,
tol_metric,
use_midpoints,
...
)
Arguments
data 
A data frame or tibble in which the columns that are given in x and class can be found. 
x 
(character) The variable name to be used for classification, e.g. predictions or test values. 
class 
(character) The variable name indicating class membership. 
metric_func 
(function) A function that computes a metric to be maximized. See description. 
pos_class 
The value of class that indicates the positive class. 
neg_class 
The value of class that indicates the negative class. 
direction 
(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class. 
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 floatingpoint problems. 
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. 
... 
Further arguments that will be passed to 
Details
The above inputs are arrived at by using all unique values in x
, Inf, or
Inf as possible cutpoints for classifying the variable in class.
Value
A tibble with the columns optimal_cutpoint
, the corresponding metric
value and roc_curve
, a nested tibble that includes all possible cutoffs
and the corresponding numbers of true and false positives / negatives and
all corresponding metric values.
See Also
Other method functions:
maximize_boot_metric()
,
maximize_gam_metric()
,
maximize_loess_metric()
,
maximize_spline_metric()
,
oc_manual()
,
oc_mean()
,
oc_median()
,
oc_youden_kernel()
,
oc_youden_normal()
Examples
cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = accuracy)
cutpointr(suicide, dsi, suicide, method = minimize_metric, metric = abs_d_sens_spec)