fast.approx

indexstrata

predictCumhaz

cpred

Implementation of various statistical models for multivariate
event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random
effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
Also contains two-stage binomial modelling that can do pairwise odds-ratio
dependence modelling based marginal logistic regression models. This is an
alternative to the alternating logistic regression approach (ALR).

Klaus Holst

mets

Analysis of Multivariate Event Times

Klaus K. Holst

Thomas Scheike 

fast.approx function

<dl><dt>time</dt>
<dd>Original ordered time points</dd>
<dt>new.time</dt>
<dd>New time points</dd>
<dt>equal</dt>
<dd>If TRUE a list is returned with additional element</dd>
<dt>type</dt>
<dd>Type of matching, nearest index, nearest greater than
or equal (right), number of elements smaller than y otherwise
the closest value above new.time is returned.</dd>
<dt>sorted</dt>
<dd>Set to true if new.time is already sorted</dd>
<dt>...</dt>
<dd>Optional additional arguments</dd></dl>

Arguments

Author

Fast approximation — fast.approx

<dl>

<dt>time</dt>
<dd>Original ordered time points</dd>


<dt>new.time</dt>
<dd>New time points</dd>


<dt>equal</dt>
<dd>If TRUE a list is returned with additional element</dd>


<dt>type</dt>
<dd>Type of matching, nearest index, nearest greater than
or equal (right), number of elements smaller than y otherwise
the closest value above new.time is returned.</dd>


<dt>sorted</dt>
<dd>Set to true if new.time is already sorted</dd>


<dt>...</dt>
<dd>Optional additional arguments</dd>

</dl>

Fast approximation

fast.approx: Fast approximation

Description

Usage

Arguments

Author

Examples