High-Performance Open-Source Archive
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_metriccutpointr 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
predictormulti_cutpointr only on all numeric columns, if
x = NULLcutpointr().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 = 0spar = NULL in
maximize_spline_metric)cutpoint_nrboot 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 = 1cutpoint_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 = cbreak_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_cutpointrplr (positive likelihood ratio),
nlr (negative likelihood ratio),
false_discovery_rate, and
false_omission_ratesilent argument for roc().cutpointr_ now accepts functions instead of character
strings as method or metricuse_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 summaryminimize_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)
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