boot_ci
now works with multiple cutpoints (multiple cutpoints are possible
if break_ties = c
).add_metric
now adds the selected metrics to the bootstrap results, too.add_metric
in summary()
.subgroup
in multi_cutpointr
to NULL
(instead of missing) to make it consistent with cutpointr
.summary_sd
so that the
various summary functions now return all values without rounding.boot_stratify
is now passed to the method functions so that the bootstrap
within maximize_boot_metric
and minimize_boot_metric
can be stratified, too.multi_cutpointr
that forced the class
variable to be
named "suicide". tibble
3.0.0)sanitize_metric
cutpointr
and roc
now both use tidyeval. !!
can be used when an argument
should be unquoted, as in dplyr
,
e.g. myvar <- "dsi"; cutpointr(suicide, !!myvar, suicide)
. cutpointr_
is now
deprecated. Transforming variables directly in the call is thus no longer
supported, e.g. cutpointr(suicide, dsi * 2, suicide)
now throws an error.multi_cutpointr
does not have the cutpointr
class
anymore. boot_ci
function is available that calculates confidence intervals
(the empirical quantiles) based on the bootstrap results.auc
function is now exported and can be used to calculate the AUC from
a cutpointr
or roc_cutpointr
object,
e.g. auc(roc(suicide, dsi, suicide, "yes", "no"))
boot_test
is a new function for carrying out a bootstrap test for
equivalence of a metric, e.g. the AUC, the Youden-Index or also the optimal
cutpoint. The standard deviation is calculated as sd
of the differences
in metric values per bootstrap repetition, then a z-test is calculated.type
argument to plot_roc
for choosing line or step roc_cutpointr
can now simply be plotted with
plot()
.errorhandling = "remove"
in foreach
.summary.cutpointr
and summary.multi_cutpointr
now print an
additional NAs
column in the bootstrap summary
and cutpointr
issues a message if any bootstrap repeats failed (e.g. because
only one class was drawn). boot_stratify
argument.summary.cutpointr
and summary.multi_cutpointr
more compactsummary.cutpointr
and summary.multi_cutpointr
any more. The rounding is now done in print.summary_cutpointr
and
print.summary_multi_cutpointr
, respectively, and can be controlled via the
digits
argumentplot_metric
has a new add_unsmoothed
argument for adding the unsmoothed
metric values to the plot as a dashed line (default TRUE
). Helpful to
inspect the smoothing of functions like maximize_gam_metric
.?oc_youden_kernel
.metric_constrain
or one of the other constrained
metrics min_constrain
can not be achieved.break_ties
in cutpointr.default
by setting it to
median
as it was already in cutpointr.numeric
and cutpointr_
.roc()
return a tibble instead of a data.frameroc()
is now possible with plot_roc()
roc_cutpointr
object with add_metric()
::
or :::
tidyr
0.8.3multi_cutpointr
objectmulti_cutpointr
, a corresponding
summary_multi_cutpointr
class and a printing method for that classvariable
is not returned anymore by multi_cutpointr
, because
it is identical to predictor
multi_cutpointr
only on all numeric columns, if x = NULL
cutpointr()
.sigfig
argument to print.cutpointr
to allow for specifying the number of
significant digits to be printedadd_metric()
function to add further metrics to the output of cutpointr()
roc01
metric function to calculate the distance of points on the ROC
curve to the point (0,1) on ROC spaceplot_sensitivity_specificity()
if boot_runs = 0
spar = NULL
in maximize_spline_metric
)cutpoint_nr
boot
column is now always returned and NA
, if no bootstrapping was
run, so that the number of returned columns is constantuse_midpoints
is now also passed to method
by cutpointr
to allow for
the calculation of midpoints within maximize_boot_metric
and minimize_boot_metric
,
which before happened in cutpointr
, leading to slightly biased cutpoints
in certain scenariosnknots
is now calculated by
stats::.nknots_smspl
and spar = 1
cutpoint_tol
argument to define a tolerance around the optimized metric,
so that multiple cutpoints in the vicinity of the target metric can be returned
and to avoid not returning other "optimal" cutpoints due to floating-point
problemsbreak_ties = c
break_ties
, the returned main metric is now not the optimal one but the
one corresponding to the summarized cutpoint (thus may be worse than the
optimal one)maximize_gam_metric
and minimize_gam_metric
for smoothing via
generalized additive modelsgeom_ribbon
now use size = 0
to plot no lines around the
(transparent) areasplot_cutpointr
plr
(positive likelihood ratio), nlr
(negative likelihood ratio),
false_discovery_rate
, and false_omission_rate
silent
argument for roc().cutpointr_
now accepts functions instead of character strings as method
or metric
use_midpoints
parameter. If TRUE (default FALSE) the
returned optimal cutpoint will be the mean of the optimal cutpoint and the next
lowest observation (for direction = ">="
) or the next highest observation
(for direction = "<="
)sum_ppvnpv
, prod_ppvnpv
, and abs_d_ppvnpv
to sum_ppv_npv
,
prod_ppv_npv
, and abs_d_ppvnpv
to match the naming scheme to the names of
the metrics that optimize sensitivity and specificitysummary_sd
function now also returns 5% and 95% percentiles that are
included in the output of summary
minimize_boot_metric
and
maximize_boot_metric
was changed from 200 to 50summary
function now returns a data.frame instead of a list, also
the printing method for summary_cutpointr
has been slightly modifiedplot_sensitivity_specificity
for plotting cutpoints vs.
sensitivity and specificity on the y-axisoc_optimalCutpoints
functionROCR
and OptimalCutpoints
by rewriting tests and
storing benchmark resultsdata
argument. Thus, it can be used as before by specifying data
, x
, and class
or alternatively without specifying data
and directly supplying the vectors
of predictions and outcomes as x
and class
.silent
argument for optionally suppressing messages (e.g. which class
is assumed to be the positive one)Add the following code to your website.
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