High-Performance Open-Source Archive
pred_val_probs(), that allows
users to validate a vector of binary outcomes against a corresponding
vector of predicted risks. Acts as a standalone way of validating
predicted risks against binary outcomes outside of the usual
pred_input_info() -> pred_validate()
workflow for logistic models.pred_validate() to allow users to
specify the confidence interval width of all performance metrics.pred_validate() and pred_val_probs() to render
the calibration plot over a subset of the data, for the purposes of
rendering speed. The calibration plot is always created using all data,
but for rendering speed in large datasets, it can sometimes be useful to
render the plot over a smaller (random) subset of observations. Final
(e.g. publication-ready) plots should always show the full plot, so a
warning is created if users use this option. For similar reasons of
rending speed, the rug on the x-axis of the calibration plot is now not
shown by default.pred_input_info() now contains checks to ensure that
variable names of model_info, and variable names of new_data passed into
other functions are ‘clean’ (i.e., dont contain spaces, punctuation,
brackets, etc.)summary.predvalidate to rename “calibration-in-the-large””
for logistic models as being the “calibration intercept”.predvalidate() now calculates observed:expected ratio
for validating logistic models, along with calibration interceptpred_validate() now stores the calibration plots
(ggplot) as part of the output (previous versions of the package just
printed the plot without outputting the plot object). This facilitates
users saving or further changing the style of the plot.NEWS.md file to track changes to the
package.
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