Package: frailtyMMpen 1.2.1
Yunpeng Zhou
frailtyMMpen: Efficient Algorithm for High-Dimensional Frailty Model
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>.
Authors:
frailtyMMpen_1.2.1.tar.gz
frailtyMMpen_1.2.1.tar.gz(r-4.5-noble)frailtyMMpen_1.2.1.tar.gz(r-4.4-noble)
frailtyMMpen_1.2.1.tgz(r-4.4-emscripten)frailtyMMpen_1.2.1.tgz(r-4.3-emscripten)
frailtyMMpen.pdf |frailtyMMpen.html✨
frailtyMMpen/json (API)
NEWS
| # Install 'frailtyMMpen' in R: |
| install.packages('frailtyMMpen', repos = 'https://cloud.r-project.org') |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:a1d0a64d78. Checks:1 OK, 2 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Apr 02 2025 |
| R-4.5-linux-x86_64 | NOTE | Apr 02 2025 |
| R-4.4-linux-x86_64 | NOTE | Apr 02 2025 |
Exports:clustereventfrailtyMMfrailtyMMpen
Dependencies:latticeMatrixmgcvnlmenumDerivRcppRcppGSLsurvival
Citation
To cite package ‘frailtyMMpen’ in publications use:
Huang X, Zhou Y (2023). frailtyMMpen: Efficient Algorithm for High-Dimensional Frailty Model. R package version 1.2.1, https://CRAN.R-project.org/package=frailtyMMpen.
Corresponding BibTeX entry:
@Manual{,
title = {frailtyMMpen: Efficient Algorithm for High-Dimensional
Frailty Model},
author = {Xifen Huang and Yunpeng Zhou},
year = {2023},
note = {R package version 1.2.1},
url = {https://CRAN.R-project.org/package=frailtyMMpen},
}
Readme and manuals
frailtyMMpen: Package for Penalized Frailty Models
This package implements the MM algorithm for a variety types of frailty models which can handle clustered data, multi-event data and recurrent data in addition to the simple frailty model. Besides, this package can obtain the estimation of parameters for penalized regression using LASSO, MCP and SCAD penalties. Currently supported frailty distributions include gamma, log-normal, inverse gaussian and PVF (1<p<2). The estimation procedure is computationally efficient which makes it also capable for handling high-dimensional data.
Installation
You can install developed version of frailtyMMpen from github with:
# install.packages("devtools")
devtools::install_github("heilokchow/frailtyMMpen")
Example
This is a basic example which shows you how to use this package, you may refer to the package manual for detailed descriptions and examples for each function.
We use the simulated data with 50 clusters and 10 objects in each cluster:
data(simdataCL)
We first run the non-penalized regression with Gamma frailty and obtain the summary statistics and the plot of conditional baseline hazard.
gam_cl = frailtyMM(Surv(time, status) ~ . + cluster(id), simdataCL, frailty = "gamma")
summary(gam_cl)
plot(gam_cl)
Then, we perform the penalized regression with Gamma frailty and LASSO penalty and obtain BIC, degree of freedom under a sequence of tuning parameters and the plot of regularization path.
gam_cl_pen = frailtyMMpen(Surv(time, status) ~ . + cluster(id), simdataCL, frailty = "gamma")
print(gam_cl_pen)
plot(gam_cl_pen)
