maximize_boot_metric {cutpointr}  R Documentation 
Optimize a metric function in binary classification after bootstrapping
Description
Given a function for computing a metric in metric_func
, these functions
bootstrap the data boot_cut
times and
maximize or minimize the metric by selecting an optimal cutpoint. The returned
optimal cutpoint is the result of applying summary_func
, e.g. the mean,
to all optimal cutpoints that were determined in the bootstrap samples.
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_boot_metric(
data,
x,
class,
metric_func = youden,
pos_class = NULL,
neg_class = NULL,
direction,
summary_func = mean,
boot_cut = 50,
boot_stratify,
inf_rm = TRUE,
tol_metric,
use_midpoints,
...
)
minimize_boot_metric(
data,
x,
class,
metric_func = youden,
pos_class = NULL,
neg_class = NULL,
direction,
summary_func = mean,
boot_cut = 50,
boot_stratify,
inf_rm = TRUE,
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 single number 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. 
summary_func 
(function) After obtaining the bootstrapped optimal cutpoints this function, e.g. mean or median, is applied to arrive at a single cutpoint. 
boot_cut 
(numeric) Number of bootstrap repetitions over which the mean optimal cutpoint is calculated. 
boot_stratify 
(logical) If the bootstrap is stratified, bootstrap samples are drawn in both classes and then combined, keeping the number of positives and negatives constant in every resample. 
inf_rm 
(logical) whether to remove infinite cutpoints before calculating the summary. 
tol_metric 
All cutpoints will be passed to 
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. 
... 
To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function. 
Details
The above inputs are arrived at by using all unique values in x
, Inf, and
Inf as possible cutpoints for classifying the variable in class.
The reported metric represents the usual insample performance of the
determined cutpoint.
Value
A tibble with the column optimal_cutpoint
See Also
Other method functions:
maximize_gam_metric()
,
maximize_loess_metric()
,
maximize_metric()
,
maximize_spline_metric()
,
oc_manual()
,
oc_mean()
,
oc_median()
,
oc_youden_kernel()
,
oc_youden_normal()
Examples
set.seed(100)
cutpointr(suicide, dsi, suicide, method = maximize_boot_metric,
metric = accuracy, boot_cut = 30)
set.seed(100)
cutpointr(suicide, dsi, suicide, method = minimize_boot_metric,
metric = abs_d_sens_spec, boot_cut = 30)