maximize_metric {cutpointr} | R Documentation |
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
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, ... )
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 floating-point 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 |
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.
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.
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()
cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = accuracy) cutpointr(suicide, dsi, suicide, method = minimize_metric, metric = abs_d_sens_spec)