RRPP: An R package for fitting linear models to high-dimensional data using residual randomization
RRPP: An R package for fitting linear models to high-dimensional data using residual randomization
复制标题
DOI：
10.1111/2041-210x.13029
复制
发表时间：
2018-07-01
影响因子：
6.6
通讯作者：
Adams, Dean C.
中科院分区：
文献类型：
--
作者：
Collyer, Michael L.;Adams, Dean C.
1.排列程序中的残差随机化（RRPP）是使用普通或广义最小二乘估计生成ANOVA统计量和线性模型系数的经验抽样分布的适当方法。这对于高维（多变量）数据是一种特别有用的方法。2.在这里，我们提出了一个r包，它提供了一套全面的工具，用于将RRPP应用于线性模型。重要的可用功能包括OLS或GLS系数估计的选择，数据或相异性矩阵分析功能，I，II或III类平方和和叉积的选择，各种效应量估计方法，以及执行混合模型ANOVA的能力。lm.rrpp函数在许多方面与lm函数类似，但提供了许多随机排列的系数和ANOVA统计估计值。通常与lm一起使用的S3泛型函数也可以与lm.rrpp一起使用。此外，成对函数提供了指定组之间最小二乘均值或斜率比较的统计检验。用户有许多不同的随机排列选项。与类似的可用软件包和功能相比，RRPP速度极快，并为下游分析和图形生成全面的结果，以下模型符合lm.rrpp.4。RRPP软件包有助于分析单变量和多变量响应数据，即使变量数量超过观察数量。
1. Residual randomization in permutation procedures (RRPP) is an appropriate means of generating empirical sampling distributions for ANOVA statistics and linear model coefficients, using ordinary or generalized least-squares estimation. This is an especially useful approach for high-dimensional (multivariate) data.2. Here, we present an r package that provides a comprehensive suite of tools for applying RRPP to linear models. Important available features include choices for OLS or GLS coefficient estimation, data or dissimilarity matrix analysis capability, choice among types I, II, or III sums of squares and cross-products, various effect size estimation methods, and an ability to perform mixed-model ANOVA.3. The lm.rrpp function is similar to the lm function in many regards, but provides coefficient and ANOVA statistics estimates over many random permutations. The S3 generic functions commonly used with lm also work with lm.rrpp. Additionally, a pairwise function provides statistical tests for comparisons of least-squares means or slopes, among designated groups. Users have many options for varying random permutations. Compared to similar available packages and functions, RRPP is extremely fast and yields comprehensive results for downstream analyses and graphics, following model fits with lm.rrpp.4. The RRPP package facilitates analysis of both univariate and multivariate response data, even when the number of variables exceeds the number of observations.