print.sl.time

Print the contribution of learners included in the <code>sl.time</code> function.

printing

Super Learner

Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.

Yohann Foucher

RISCA

Causal Inference and Prediction in Cohort-Based Analyses

Florent Le Borgne

Arthur Chatton

Camille Sabathe

print.sl.time function

<dl> <dt>x</dt>
<dd>An object returned by the function <code>sl.time</code>.</dd> <dt>...</dt>
<dd>Further arguments passed.</dd> <dt>digits</dt>
<dd>An optional integer for the number of digits to print when printing numeric values.</dd></dl>

Arguments

Yohann Foucher &lt;Yohann.Foucher@univ-poitiers.fr&gt;
Camille Sabathe &lt;camille.sabathe@univ-nantes.fr&gt;

Author

S3 method for Printing an 'sl.time' object — print.sl.time

<dl>

 <dt>x</dt>
<dd>An object returned by the function <code>sl.time</code>.</dd>

 <dt>...</dt>
<dd>Further arguments passed.</dd>

 <dt>digits</dt>
<dd>An optional integer for the number of digits to print when printing numeric values.</dd>

</dl>

print.sl.time: S3 method for Printing an 'sl.time' object

Description

Usage

Arguments

Author

Examples