glmmPen:高维的惩罚性通用化的线性混合模型
Hillary M Heiling1, Naim U Rashid1, Quefeng Li1
1University of North Carolina Chapel Hill.
glmmPen R包允许在高维通用线性混合模型 (GLMM) 中同时选择固定和随机效应. 这种方法克服了传统方法的局限性,提高了复杂数据集的模型准确性.
科学领域:
- 统计 统计 统计 统计
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 一般化的线性混合模型 (GLMMs) 对于分析相关的非高斯数据至关重要.
- 准确选择固定和随机效应至关重要,以防止GLMM中的偏差.
- 以前的联合效应选择方法仅限于较低维度的问题.
研究的目的:
- 介绍R包glmmPen用于高维的GLMM.
- 为联合固定和随机效应选择开发一个处罚模型框架.
- 为参数估计提供一个高效的计算算法.
主要方法:
- 使用处罚的通用线性混合模型框架.
- 使用蒙特卡洛预期条件最小化 (MCECM) 算法进行参数估计.
- 在glmmPen包中利用Stan和RcppArmadillo提高计算效率.
主要成果:
- glmmPen软件包有助于在高维的GLMM中联合选择固定和随机效果.
- 在MCECM算法提供高效的参数估计.
- 模拟显示在选择固定和随机效应方面表现良好.
结论:
- glmmPen为高维的GLMM提供了一个新的解决方案,解决了效果选择的局限性.
- 该包支持双项式,高斯式和波松式家族,具有各种惩罚功能.
- 这种方法提高了GLMM分析在复杂研究领域的准确性和可靠性.
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