simdataCL

<p>This data include 50 clusters with 10 objects, a total of 100 events are recorded.
30 temporal covariates can be used for model fitting. The data is generated from a
gamma frailty model with coefficients (1,2,3,4,0,...,0) and frailty variance 1.</p>

datasets

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Yunpeng Zhou

frailtyMMpen

Efficient Algorithm for High-Dimensional Frailty Model

Xifen Huang

Jinfeng Xu

simdataCL function

<p>An object of class <code>data.frame</code> with 500 rows and 33 columns.</p>

simdataCL: Simulated Clustered data

Description

Usage

Arguments

Format

Examples