phecap_plot_roc_curves

either a single object of class PhecapModel or PhecapValidation
(returned from <code>phecap_train_phenotyping_model</code> or
<code>phecap_validate_phenotyping_model</code>), or a named list of such objects

an expression that leads to the <code>x</code> coordinate.
Recognized quantities include:
<code>cut</code> (probability cutoff),
<code>pct</code> (percent of predicted cases),
<code>acc</code> (accuracy),
<code>tpr</code> (true positive rate),
<code>fpr</code> (false positive rate),
<code>tnr</code> (true negative rate),
<code>ppv</code> (positive predictive value),
<code>fdr</code> (false discovery rate),
<code>npv</code> (negative predictive value),
<code>sen</code> (sensitivity),
<code>spec</code> (specificity),
<code>prec</code> (precision),
<code>rec</code> (recall),
<code>f1</code> (F1 score).

axis_x

an expression that leads to the <code>y</code> coordinate.
Recognized quantities are the same as those in <code>axis_x</code>.

axis_y

The curves to be included in the figure.

what

if TRUE and ggplot2 is installed, ggplot will be used for the figure.
Otherwise, the base R graphics functions will be used.

ggplot

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Plot ROC-like curves to illustrate phenotyping accuracy.

Implement surrogate-assisted feature extraction (SAFE) and
common machine learning approaches to train and validate phenotyping models.
Background and details about the methods can be found at
Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>,
Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and
Liao et al. (2015) <doi:10.1136/bmj.h1885>.

phecap_plot_roc_curves: Plot ROC and Related Curves for Phenotyping Models

Description

Usage

Arguments

See Also