maximize_boot_metric {cutpointr} | R Documentation |
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
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, ... )
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. |
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 in-sample performance of the
determined cutpoint.
A tibble with the column optimal_cutpoint
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()
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)