IPCweights

Compute weights for censored regression via the inverted 
 probability of censoring principle.

survival

Functional gradient descent algorithm
(boosting) for optimizing general risk functions utilizing
component-wise (penalised) least squares estimates or regression
trees as base-learners for fitting generalized linear, additive
and interaction models to potentially high-dimensional data.
Models and algorithms are described in <doi:10.1214/07-STS242>,
a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>.
The package allows user-specified loss functions and base-learners.

Torsten Hothorn

mboost

Model-Based Boosting

Peter Buehlmann

Thomas Kneib

Matthias Schmid

Benjamin Hofner

Fabian Otto-Sobotka

Fabian Scheipl

Andreas Mayr

IPCweights function

<dl> <dt>x</dt>
<dd>an object of class <code>Surv</code>.</dd> <dt>maxweight</dt>
<dd>the maximal value of the returned weights.</dd></dl>

Arguments

Inverse Probability of Censoring Weights — IPCweights

<dl>

 <dt>x</dt>
<dd>an object of class <code>Surv</code>.</dd>

 <dt>maxweight</dt>
<dd>the maximal value of the returned weights.</dd>

</dl>

Inverse Probability of Censoring Weights

IPCweights: Inverse Probability of Censoring Weights

Description

Usage

Value

Arguments

Details

References