print.logistic_calsummary

Fit calibrations curves for clinical prediction models and calculate several associated
metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model
should align with observed probabilities. Calibration curves relate predicted probabilities
(or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function.
'pmcalibration' allows users to choose between several smoothers (regression splines, generalized
additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported.
See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>;
Austin and Steyerberg (2019) <doi:10.1002/sim.8281>;
Austin et al. (2020) <doi:10.1002/sim.8570>.

Stephen Rhodes

pmcalibration

Calibration Curves for Clinical Prediction Models

print.logistic_calsummary function

<dl><dt>x</dt>
<dd>a <code>logistic_calsummary</code> object</dd>
<dt>digits</dt>
<dd>number of digits to print</dd>
<dt>...</dt>
<dd>ignored</dd></dl>

Arguments

Print a logistic_cal summary — print.logistic_calsummary

<dl>

<dt>x</dt>
<dd>a <code>logistic_calsummary</code> object</dd>


<dt>digits</dt>
<dd>number of digits to print</dd>


<dt>...</dt>
<dd>ignored</dd>

</dl>

print.logistic_calsummary: Print a logistic_cal summary

Description

Usage

Value

Arguments