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mixedbayes

Bayesian Longitudinal Regularized Quantile Mixed Model

https://github.com/kunfa/mixedbayes

Science Score: 39.0%

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Repository

Bayesian Longitudinal Regularized Quantile Mixed Model

Basic Info
  • Host: GitHub
  • Owner: kunfa
  • Language: C++
  • Default Branch: main
  • Homepage:
  • Size: 40.1 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 3 years ago · Last pushed 11 months ago
Metadata Files
Readme Changelog

README.md

mixedBayes

Bayesian Longitudinal Regularized Quantile Mixed Model <!-- badges: start -->

CRAN CRAN RStudio mirror downloads CRAN RStudio mirror downloads

With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025)). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

How to install

  • To install from github, run these two lines of code in R
install.packages("devtools")
devtools::install_github("kunfa/mixedBayes")
  • Released versions of mixedBayes are available on CRAN (link), and can be installed within R via
install.packages("mixedBayes")

Examples

Example.1 (default method: robust sparse bi-level selection under random intercept -and- slope model)

library(mixedBayes)
data(data)

fit = mixedBayes(y,e,X,g,w,k,structure=c("bi-level"))
fit$coefficient
b = selection(fit,sparse=TRUE)
index = which(coeff!=0)
pos = which(b != 0)
tp = length(intersect(index, pos))
fp = length(pos) - tp
list(tp=tp, fp=fp)

Example.2 (alternative: robust sparse individual level selection under random intercept -and- slope model)

fit = mixedBayes(y,e,X,g,w,k,structure=c("individual"))
fit$coefficient
b = selection(fit,sparse=TRUE)
index = which(coeff!=0)
pos = which(b != 0)
tp = length(intersect(index, pos))
fp = length(pos) - tp
list(tp=tp, fp=fp)

Example.3 (alternative: non-robust sparse bi-level selection under random intercept -and- slope model)

fit = mixedBayes(y,e,X,g,w,k,robust=FALSE, structure=c("bi-level"))
fit$coefficient
b = selection(fit,sparse=TRUE)
index = which(coeff!=0)
pos = which(b != 0)
tp = length(intersect(index, pos))
fp = length(pos) - tp
list(tp=tp, fp=fp)

Example.4 (alternative: robust sparse bi-level selection under random intercept model)

fit = mixedBayes(y,e,X,g,w,k,slope=FALSE, structure=c("bi-level"))
fit$coefficient    
b = selection(fit,sparse=TRUE)
index = which(coeff!=0)
pos = which(b != 0)
tp = length(intersect(index, pos))
fp = length(pos) - tp
list(tp=tp, fp=fp)

Methods

This package provides implementation for methods proposed in

Owner

  • Name: Kun Fan
  • Login: kunfa
  • Kind: user
  • Location: Manhattan, KS
  • Company: Kansas State University

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 305 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 13
  • Total maintainers: 1
cran.r-project.org: mixedBayes

Bayesian Longitudinal Regularized Quantile Mixed Model

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 305 Last month
Rankings
Forks count: 28.8%
Dependent packages count: 29.5%
Dependent repos count: 35.2%
Stargazers count: 35.2%
Average: 43.6%
Downloads: 89.3%
Maintainers (1)
kfan@ksu.edu
Last synced: 10 months ago