Using predictions (or e.g. biological marker values) and binary class labels, this function
will determine "optimal" cutpoints using various selectable methods. The
methods for cutpoint determination can be evaluated using bootstrapping. An
estimate of the cutpoint variability and the out-of-sample performance can then
be returned with summary or plot. For an introduction to the
package please see vignette("cutpointr", package = "cutpointr").
cutpointr(...)# S3 method for default
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,
...
)
# S3 method for numeric
cutpointr(
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,
...
)
Further optional arguments that will be passed to method. minimize_metric and maximize_metric pass ... to metric.
A data.frame with the data needed for x, class and optionally subgroup.
The variable name to be used for classification, e.g. predictions. The raw vector of values if the data argument is unused.
The variable name indicating class membership. If the data argument is unused, the vector of raw numeric values.
An additional covariate that identifies subgroups or the raw data if data = NULL. Separate optimal cutpoints will be determined per group. Numeric, character and factor are allowed.
(function) A function for determining cutpoints. Can be user supplied or use some of the built in methods. See details.
(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.
(optional) The value of class that indicates the positive class.
(optional) The value of class that indicates the negative class.
(character, optional) Use ">=" or "<=" to indicate whether x is supposed to be larger or smaller for the positive class.
(numerical) If positive, this number of bootstrap samples will be used to assess the variability and the out-of-sample performance.
(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.
(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.
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.
(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.
(logical) If TRUE, the bootstrapping will be parallelized using foreach. A local cluster, for example, should be started manually beforehand.
(logical) If TRUE suppresses all messages.
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 method functions, see details.
A cutpointr object which is also a data.frame and tbl_df.
If direction and/or pos_class and neg_class are not given, the function will
assume that higher values indicate the positive class and use the class
with a higher median as the positive class.
This function uses tidyeval to support unquoted arguments. For programming
with cutpointr the operator !! can be used to unquote an argument, see the
examples.
Different methods can be selected for determining the optimal cutpoint via the method argument. The package includes the following method functions:
maximize_metric: Maximize the metric function
minimize_metric: Minimize the metric function
maximize_loess_metric: Maximize the metric function after LOESS
smoothing
minimize_loess_metric: Minimize the metric function after LOESS
smoothing
maximize_spline_metric: Maximize the metric function after spline
smoothing
minimize_spline_metric: Minimize the metric function after spline
smoothing
maximize_boot_metric: Maximize the metric function as a summary of
the optimal cutpoints in bootstrapped samples
minimize_boot_metric: Minimize the metric function as a summary of
the optimal cutpoints in bootstrapped samples
oc_youden_kernel: Maximize the Youden-Index after kernel smoothing
the distributions of the two classes
oc_youden_normal: Maximize the Youden-Index parametrically
assuming normally distributed data in both classes
oc_manual: Specify the cutpoint manually
User-defined functions can be supplied to method, too. As a reference, the code of all included method functions can be accessed by simply typing their name. To define a new method function, create a function that may take as input(s):
data: A data.frame or tbl_df
x: (character) The name of the predictor or independent variable
class: (character) The name of the class or dependent variable
metric_func: A function for calculating a metric, e.g. accuracy
pos_class: The positive class
neg_class: The negative class
direction: ">=" if the positive class has higher x values, "<=" otherwise
tol_metric: (numeric) In the built-in methods a tolerance around
the optimal metric value
use_midpoints: (logical) In the built-in methods whether to
use midpoints instead of exact optimal cutpoints
... Further arguments
The ... argument can be used to avoid an error if not all of the above
arguments are needed or in order to pass additional arguments to method.
The function should return a data.frame or tbl_df with
one row, the column "optimal_cutpoint", and an optional column with an arbitrary name
with the metric value at the optimal cutpoint.
Built-in metric functions include:
accuracy: Fraction correctly classified
youden: Youden- or J-Index = sensitivity + specificity - 1
sum_sens_spec: sensitivity + specificity
sum_ppv_npv: The sum of positive predictive value (PPV) and negative
predictive value (NPV)
prod_sens_spec: sensitivity * specificity
prod_ppv_npv: The product of positive predictive value (PPV) and
negative predictive value (NPV)
cohens_kappa: Cohen's Kappa
abs_d_sens_spec: The absolute difference between
sensitivity and specificity
roc01: Distance to the point (0,1) on ROC space
abs_d_ppv_npv: The absolute difference between positive predictive
value (PPV) and negative predictive value (NPV)
p_chisquared: The p-value of a chi-squared test on the confusion
matrix of predictions and observations
odds_ratio: The odds ratio calculated as (TP / FP) / (FN / TN)
risk_ratio: The risk ratio (relative risk) calculated as
(TP / (TP + FN)) / (FP / (FP + TN))
positive and negative likelihood ratio calculated as
plr = true positive rate / false positive rate and
nlr = false negative rate / true negative rate
misclassification_cost: The sum of the misclassification cost of
false positives and false negatives fp * cost_fp + fn * cost_fn.
Additional arguments to cutpointr: cost_fp, cost_fn
total_utility: The total utility of true / false positives / negatives
calculated as utility_tp * TP + utility_tn * TN - cost_fp * FP - cost_fn * FN.
Additional arguments to cutpointr: utility_tp, utility_tn,
cost_fp, cost_fn
F1_score: The F1-score (2 * TP) / (2 * TP + FP + FN)
sens_constrain: Maximize sensitivity given a minimal value of
specificity
spec_constrain: Maximize specificity given a minimal value of
sensitivity
metric_constrain: Maximize a selected metric given a minimal
value of another selected metric
Furthermore, the following functions are included which can be used as metric
functions but are more useful for plotting purposes, for example in
plot_cutpointr, or for defining new metric functions:
tp, fp, tn, fn, tpr, fpr,
tnr, fnr, false_omission_rate,
false_discovery_rate, ppv, npv, precision,
recall, sensitivity, and specificity.
User defined metric functions can be created as well which can accept the following inputs as vectors:
tp: Vector of true positives
fp: Vector of false positives
tn: Vector of true negatives
fn: Vector of false negatives
... If the metric function is used in conjunction with any of the
maximize / minimize methods, further arguments can be passed
The function should return a numeric vector or a matrix or a data.frame
with one column. If the column is named,
the name will be included in the output and plots. Avoid using names that
are identical to the column names that are by default returned by cutpointr.
If boot_runs is positive, that number of bootstrap samples will be drawn
and the optimal cutpoint using method will be determined. Additionally,
as a way of internal validation, the function in metric will be used to
score the out-of-bag predictions using the cutpoints determined by
method. Various default metrics are always included in the bootstrap results.
If multiple optimal cutpoints are found, the column optimal_cutpoint becomes a list that contains the vector(s) of the optimal cutpoints.
If use_midpoints = TRUE the mean of the optimal cutpoint and the next
highest or lowest possible cutpoint is returned, depending on direction.
The tol_metric argument can be used to avoid floating-point problems
that may lead to exclusion of cutpoints that achieve the optimally achievable
metric value. Additionally, by selecting a large tolerance multiple cutpoints
can be returned that lead to decent metric values in the vicinity of the
optimal metric value. tol_metric is passed to metric and is only
supported by the maximization and minimization functions, i.e.
maximize_metric, minimize_metric, maximize_loess_metric,
minimize_loess_metric, maximize_spline_metric, and
minimize_spline_metric. In maximize_boot_metric and
minimize_boot_metric multiple optimal cutpoints will be passed to the
summary_func of these two functions.
Other main cutpointr functions:
add_metric(),
boot_ci(),
boot_test(),
multi_cutpointr(),
predict.cutpointr(),
roc()
# NOT RUN {
library(cutpointr)
## Optimal cutpoint for dsi
data(suicide)
opt_cut <- cutpointr(suicide, dsi, suicide)
opt_cut
s_opt_cut <- summary(opt_cut)
plot(opt_cut)
# }
# NOT RUN {
## Predict class for new observations
predict(opt_cut, newdata = data.frame(dsi = 0:5))
## Supplying raw data, same result
cutpointr(x = suicide$dsi, class = suicide$suicide)
## direction, class labels, method and metric can be defined manually
## Again, same result
cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes",
method = maximize_metric, metric = youden)
## Optimal cutpoint for dsi, as before, but for the separate subgroups
opt_cut <- cutpointr(suicide, dsi, suicide, gender)
opt_cut
(s_opt_cut <- summary(opt_cut))
tibble:::print.tbl(s_opt_cut)
## Bootstrapping also works on individual subgroups
set.seed(30)
opt_cut <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 1000,
boot_stratify = TRUE)
opt_cut
summary(opt_cut)
plot(opt_cut)
## Parallelized bootstrapping
library(doParallel)
library(doRNG)
cl <- makeCluster(2) # 2 cores
registerDoParallel(cl)
registerDoRNG(12) # Reproducible parallel loops using doRNG
opt_cut <- cutpointr(suicide, dsi, suicide, gender,
boot_runs = 1000, allowParallel = TRUE)
stopCluster(cl)
opt_cut
plot(opt_cut)
## Robust cutpoint method using kernel smoothing for optimizing Youden-Index
opt_cut <- cutpointr(suicide, dsi, suicide, gender,
method = oc_youden_kernel)
opt_cut
# }
# NOT RUN {
# }
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