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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 steproc_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)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.