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mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models 课程5:使用高斯有限混合模型的聚类、分类和密度估计

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2016-08-01 DOI: 10.32614/RJ-2016-021

L. Scrucca, Michael Fop, T. B. Murphy, A. Raftery

Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.

有限混合模型越来越多地被用于模拟各种随机现象，用于聚类、分类和密度估计。mclust是一个强大而流行的软件包，它允许将数据建模为具有不同协方差结构和不同数量混合成分的高斯有限混合物，用于各种分析目的。最近，该包的第5版已经在CRAN上发布。这个更新版本增加了新的协方差结构、可视化降维能力、模型选择标准、EM算法的初始化策略和基于自引导的推理，使其成为一个功能齐全的R包，可通过有限混合建模进行数据分析。

引用次数: 1758

R Package imputeTestbench to Compare Imputation Methods for Univariate Time Series R包imputeTestbench来比较单变量时间序列的Imputation方法

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2016-08-01 DOI: 10.32614/RJ-2018-024

M. Beck, N. Bokde, G. Asencio-Cortés, K. Kulat

Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are included with the package that vary from simple replacement with means to more complex interpolation methods. The testbench is not limited to the default functions and users can add or remove methods as needed. Plotting functions also allow comparative visualization of the behavior and effectiveness of different algorithms. We present example applications that demonstrate how the package can be used to understand differences in prediction accuracy between methods as affected by characteristics of a dataset and the nature of missing data.

缺失观测值在时间序列数据中很常见，有几种方法可用于在分析之前推断这些值。单变量时间序列统计特征的变化会对缺失观测值的特征产生深远的影响，从而影响不同估算方法的准确性。对于用户提供的数据集，可以使用imputeTestbench包来比较与缺失数据的数量和类型相关的不同方法的预测精度。通过完全随机地或根据数据的特征以不同大小的块移除观测值来模拟缺失的数据。几种插值算法包含在包中，从简单的替换手段到更复杂的插值方法。测试平台不局限于默认函数，用户可以根据需要添加或删除方法。绘图函数还允许对不同算法的行为和有效性进行比较可视化。我们给出的示例应用程序演示了如何使用该包来理解受数据集特征和缺失数据性质影响的方法之间的预测准确性差异。

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引用次数: 16

fslr: Connecting the FSL Software with R. fslr：用 R 连接 FSL 软件

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2015-06-01

John Muschelli, Elizabeth Sweeney, Martin Lindquist, Ciprian Crainiceanu

We present the package fslr, a set of R functions that interface with FSL (FMRIB Software Library), a commonly-used open-source software package for processing and analyzing neuroimaging data. The fslr package performs operations on 'nifti' image objects in R using command-line functions from FSL, and returns R objects back to the user. fslr allows users to develop image processing and analysis pipelines based on FSL functionality while interfacing with the functionality provided by R. We present an example of the analysis of structural magnetic resonance images, which demonstrates how R users can leverage the functionality of FSL without switching to shell commands.

我们介绍了fslr软件包，它是一组与FSL（FMRIB软件库）接口的R函数，FSL是处理和分析神经成像数据的常用开源软件包。fslr软件包使用FSL的命令行函数对R语言中的 "nifti "图像对象执行操作，并将R语言对象返回给用户。fslr允许用户开发基于FSL功能的图像处理和分析管道，同时与R语言提供的功能接口。

引用次数: 0

fslr: Connecting the FSL Software with R fslr:连接FSL软件与R

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2015-01-01 DOI: 10.32614/RJ-2015-013

J. Muschelli, E. Sweeney, M. Lindquist, C. Crainiceanu

We present the package fslr, a set of R functions that interface with FSL (FMRIB Software Library), a commonly-used open-source software package for processing and analyzing neuroimaging data. The fslr package performs operations on 'nifti' image objects in R using command-line functions from FSL, and returns R objects back to the user. fslr allows users to develop image processing and analysis pipelines based on FSL functionality while interfacing with the functionality provided by R. We present an example of the analysis of structural magnetic resonance images, which demonstrates how R users can leverage the functionality of FSL without switching to shell commands.

我们提出了fslr包，这是一组与FSL (FMRIB软件库)接口的R函数，FSL是一个常用的用于处理和分析神经成像数据的开源软件包。fslr包使用FSL中的命令行函数在R中对'nifti'图像对象执行操作，并将R对象返回给用户。fslr允许用户基于FSL功能开发图像处理和分析管道，同时与R提供的功能进行接口。我们给出了一个结构磁共振图像分析的示例，该示例演示了R用户如何利用FSL的功能，而无需切换到shell命令。

引用次数: 30

Stratified Weibull Regression Model for Interval-Censored Data. 区间截尾数据的分层威布尔回归模型。

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2014-06-01

Xiangdong Gu, David Shapiro, Michael D Hughes, Raji Balasubramanian

Interval censored outcomes arise when a silent event of interest is known to have occurred within a specific time period determined by the times of the last negative and first positive diagnostic tests. There is a rich literature on parametric and non-parametric approaches for the analysis of interval-censored outcomes. A commonly used strategy is to use a proportional hazards (PH) model with the baseline hazard function parameterized. The proportional hazards assumption can be relaxed in stratified models by allowing the baseline hazard function to vary across strata defined by a subset of explanatory variables. In this paper, we describe and implement a new R package straweib, for fitting a stratified Weibull model appropriate for interval censored outcomes. We illustrate the R package straweib by analyzing data from a longitudinal oral health study on the timing of the emergence of permanent teeth in 4430 children.

当已知感兴趣的无声事件发生在由最后一次阴性和第一次阳性诊断测试的时间决定的特定时间段内时，就会出现区间审查结果。关于用于分析区间截尾结果的参数和非参数方法，有丰富的文献。一种常用的策略是使用比例危险（PH）模型，并将基线危险函数参数化。在分层模型中，可以通过允许基线风险函数在解释变量子集定义的各层之间变化来放松比例风险假设。在本文中，我们描述并实现了一种新的R包straweib，用于拟合适用于区间截尾结果的分层威布尔模型。我们通过分析4430名儿童恒牙出现时间的纵向口腔健康研究数据来说明R包straweib。

引用次数: 0

brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data. brainR:交互式三维和四维图像的高分辨率神经图像数据。

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2014-06-01

John Muschelli, Elizabeth Sweeney, Ciprian Crainiceanu

We provide software tools for displaying and publishing interactive 3-dimensional (3D) and 4-dimensional (4D) figures to html webpages, with examples of high-resolution brain imaging. Our framework is based in the R statistical software using the rgl package, a 3D graphics library. We build on this package to allow manipulation of figures including rotation and translation, zooming, coloring of brain substructures, adjusting transparency levels, and addition/or removal of brain structures. The need for better visualization tools of ultra high dimensional data is ever present; we are providing a clean, simple, web-based option. We also provide a package (brainR) for users to readily implement these tools.

我们提供软件工具，用于在html网页上显示和发布交互式三维(3D)和四维(4D)图形，并提供高分辨率脑成像示例。我们的框架是基于R统计软件，使用rgl包，一个3D图形库。我们建立在这个包上，允许操纵数字，包括旋转和翻译，缩放，大脑子结构的着色，调整透明度水平，以及添加/或移除大脑结构。对超高维数据的更好的可视化工具的需求一直存在;我们提供了一个干净、简单、基于网络的选择。我们还为用户提供了一个包(brainR)来方便地实现这些工具。

引用次数: 0

Stratified Weibull Regression Model for Interval-Censored Data 区间截尾数据的分层威布尔回归模型

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2014-06-01 DOI: 10.32614/RJ-2014-003

Xiangdong Gu, D. Shapiro, M. Hughes, R. Balasubramanian

Interval censored outcomes arise when a silent event of interest is known to have occurred within a specific time period determined by the times of the last negative and first positive diagnostic tests. There is a rich literature on parametric and non-parametric approaches for the analysis of interval-censored outcomes. A commonly used strategy is to use a proportional hazards (PH) model with the baseline hazard function parameterized. The proportional hazards assumption can be relaxed in stratified models by allowing the baseline hazard function to vary across strata defined by a subset of explanatory variables. In this paper, we describe and implement a new R package straweib, for fitting a stratified Weibull model appropriate for interval censored outcomes. We illustrate the R package straweib by analyzing data from a longitudinal oral health study on the timing of the emergence of permanent teeth in 4430 children.

当已知在由最后一次阴性和第一次阳性诊断试验的时间确定的特定时间段内发生了感兴趣的沉默事件时，就会出现间隔审查结果。关于区间截尾结果分析的参数方法和非参数方法有丰富的文献。一种常用的策略是使用基线风险函数参数化的比例风险(PH)模型。在分层模型中，通过允许基线风险函数在由解释变量子集定义的各层之间变化，可以放宽比例风险假设。在本文中，我们描述并实现了一个新的R包straweib，用于拟合适合于区间删节结果的分层威布尔模型。我们通过分析一项关于4430名儿童恒牙出现时间的纵向口腔健康研究的数据来说明R包吸管。

引用次数: 3

brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data brainR:交互式三维和四维图像的高分辨率神经图像数据

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2014-06-01 DOI: 10.32614/RJ-2014-004

J. Muschelli, E. Sweeney, C. Crainiceanu

We provide software tools for displaying and publishing interactive 3-dimensional (3D) and 4-dimensional (4D) figures to html webpages, with examples of high-resolution brain imaging. Our framework is based in the R statistical software using the rgl package, a 3D graphics library. We build on this package to allow manipulation of figures including rotation and translation, zooming, coloring of brain substructures, adjusting transparency levels, and addition/or removal of brain structures. The need for better visualization tools of ultra high dimensional data is ever present; we are providing a clean, simple, web-based option. We also provide a package (brainR) for users to readily implement these tools.

引用次数: 11

RNetCDF – A Package for Reading and Writing NetCDF Datasets 一个用于读写NetCDF数据集的包

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2013-12-01 DOI: 10.7892/BORIS.47220

Pavel Michna, Milton Woods

This paper describes the RNetCDF package (version 1.6), an interface for reading and writing files in Unidata NetCDF format, and gives an introduction to the NetCDF file format. NetCDF is a machine independent binary file format which allows storage of different types of array based data, along with short metadata descriptions. The package presented here allows access to the most important functions of the NetCDF C-interface for reading, writing, and modifying NetCDF datasets. In this paper, we present a short overview on the NetCDF file format and show usage examples of the package.

本文介绍了RNetCDF包(version 1.6)，一个读写Unidata NetCDF格式文件的接口，并对NetCDF文件格式进行了介绍。NetCDF是一种独立于机器的二进制文件格式，它允许存储不同类型的基于数组的数据，以及简短的元数据描述。这里提供的包允许访问NetCDF c接口的最重要的功能，用于读取、写入和修改NetCDF数据集。在本文中，我们简要介绍了NetCDF文件格式，并展示了该包的使用示例。

引用次数: 21

Fast Pure R Implementation of GEE: Application of the Matrix Package GEE的快速纯R实现:矩阵包的应用

IF 2.1 4区计算机科学 Q2 Mathematics

R Journal

Pub Date : 2013-06-01 DOI: 10.32614/RJ-2013-017

Lee S McDaniel, Nicholas C Henderson, P. Rathouz

Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.

R中的广义估计方程求解器只允许为链接函数和方差函数提供一些预先确定的选项。我们提供了一个包geeM，它完全用R实现，允许用户指定链接和方差函数。matrix包中提供的稀疏矩阵表示支持快速实现。为了提高速度，我们尽可能使用工作相关的解析逆，并在无法使用解析逆时使用快速求数值逆的技巧。通过三个示例，我们演示了geeM的速度，它在小数据集上并不比gepack和gee等C实现差多少，在大数据集上更快。

引用次数: 47

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