AIC.CopulaCenR

Copula-based regression models for multivariate censored data, including
bivariate right-censored data, bivariate interval-censored data, and right/interval-censored
semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and
Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic,
Gompertz) and semiparametric (Cox and transformation) models. Includes methods for
convenient prediction and plotting. Also provides a bivariate time-to-event simulation
function and an information ratio-based goodness-of-fit test for copula. Method details
can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics,
Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and
Sun et al. (2023+) JRSSC.

Tao Sun

CopulaCenR

Copula-Based Regression Models for Multivariate Censored Data

AIC.CopulaCenR function

<dl><dt>object</dt>
<dd>a CopulaCenR object</dd>
<dt>...</dt>
<dd>further arguments</dd>
<dt>k</dt>
<dd>numeric, with k = 2 for AIC</dd></dl>

Arguments

the AIC of a CopulaCenR object — AIC.CopulaCenR

<dl>

<dt>object</dt>
<dd>a CopulaCenR object</dd>


<dt>...</dt>
<dd>further arguments</dd>


<dt>k</dt>
<dd>numeric, with k = 2 for AIC</dd>

</dl>

AIC.CopulaCenR: the AIC of a CopulaCenR object

Description

Usage

Arguments