R Journal最新文献_第10页

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming Ckmeans.1d。动态规划的一维最优k-均值聚类

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal

Pub Date : 2011-12-01 DOI: 10.32614/RJ-2011-015

Haizhou Wang, Mingzhou Song

The heuristic k-means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp. We demonstrate its advantage in optimality and runtime over the standard iterative k-means algorithm.

广泛用于聚类分析的启发式k-均值算法不能保证最优性。我们开发了一种动态规划算法来优化一维聚类。该算法是作为一个名为Ckmeans.1d.dp的R包实现的。我们证明了它在最优性和运行时间上优于标准迭代k-means算法。

引用次数: 332

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming. Ckmeans.1d.dp:动态规划的一维最优k均值聚类。

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal

Pub Date : 2011-12-01

Haizhou Wang, Mingzhou Song

The heuristic k-means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp. We demonstrate its advantage in optimality and runtime over the standard iterative k-means algorithm.

启发式k-均值算法被广泛用于聚类分析，但不能保证最优性。我们开发了一种用于最优一维聚类的动态规划算法。该算法被实现为一个名为Ckmeans.1d.dp的R包。我们证明了它在最优性和运行时间方面优于标准迭代k-means算法。

引用次数: 0

binGroup: A Package for Group Testing binGroup:用于组测试的软件包

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal

Pub Date : 2010-12-01 DOI: 10.32614/RJ-2010-016

C. Bilder, Boan Zhang, F. Schaarschmidt, J. Tebbs

When the prevalence of a disease or of some other binary characteristic is small, group testing (also known as pooled testing) is frequently used to estimate the prevalence and/or to identify individuals as positive or negative. We have developed the binGroup package as the first package designed to address the estimation problem in group testing. We present functions to estimate an overall prevalence for a homogeneous population. Also, for this setting, we have functions to aid in the very important choice of the group size. When individuals come from a heterogeneous population, our group testing regression functions can be used to estimate an individual probability of disease positivity by using the group observations only. We illustrate our functions with data from a multiple vector transfer design experiment and a human infectious disease prevalence study.

当某种疾病或某些其他二元特征的流行率很小时，通常使用群体检测(也称为合并检测)来估计流行率和/或确定个体为阳性或阴性。我们已经开发了binGroup包，作为第一个设计用于解决组测试中的评估问题的包。我们提出了估算同质人群总体患病率的函数。此外，对于这种设置，我们有一些函数来帮助选择非常重要的组大小。当个体来自异质群体时，我们的群体检验回归函数可用于仅使用群体观察值来估计个体疾病阳性的概率。我们用多载体转移设计实验和人类传染病流行研究的数据来说明我们的函数。

引用次数: 36

binGroup: A Package for Group Testing. binGroup:用于组测试的软件包。

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal

Pub Date : 2010-12-01

Christopher R Bilder, Boan Zhang, Frank Schaarschmidt, Joshua M Tebbs

When the prevalence of a disease or of some other binary characteristic is small, group testing (also known as pooled testing) is frequently used to estimate the prevalence and/or to identify individuals as positive or negative. We have developed the binGroup package as the first package designed to address the estimation problem in group testing. We present functions to estimate an overall prevalence for a homogeneous population. Also, for this setting, we have functions to aid in the very important choice of the group size. When individuals come from a heterogeneous population, our group testing regression functions can be used to estimate an individual probability of disease positivity by using the group observations only. We illustrate our functions with data from a multiple vector transfer design experiment and a human infectious disease prevalence study.

当某种疾病或某些其他二元特征的流行率很小时，通常使用群体检测(也称为合并检测)来估计流行率和/或确定个体为阳性或阴性。我们已经开发了binGroup包，作为第一个设计用于解决组测试中的评估问题的包。我们提出了估算同质人群总体患病率的函数。此外，对于这种设置，我们有一些函数来帮助选择非常重要的组大小。当个体来自异质群体时，我们的群体检验回归函数可用于仅使用群体观察值来估计个体疾病阳性的概率。我们用多载体转移设计实验和人类传染病流行研究的数据来说明我们的函数。

引用次数: 0

mvtnorm: New numerical algorithm for multivariate normal probabilities mvtnorm:多元正态概率的新数值算法

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal

Pub Date : 2009-01-01 DOI: 10.15488/3835

Xuefei Mi, Tetsuhisa Miwa, T. Hothorn

Miwa et al. (2003) proposed a numerical algorithm for evaluating multivariate normal probabilities. Starting with version 0.9-0 of the mvtnorm package (Hothorn et al., 2001; Genz et al., 2008), this algorithm is available to the R community. We give a brief introduction to Miwa’s procedure and compare it, with respect to computing time and accuracy, to a quasi-randomized Monte-Carlo procedure proposed by Genz and Bretz (1999), which has been available through mvtnorm for some years now. The new algorithm is applicable to problems with dimension smaller than 20, whereas the procedures by Genz and Bretz (1999) can be used to evaluate 1000-dimensional normal distributions. At the end of this article, a suggestion is given for choosing a suitable algorithm in different situations.

Miwa等人(2003)提出了一种评估多元正态概率的数值算法。从mvtnorm包的0.9-0版本开始(Hothorn et al.， 2001;Genz et al.， 2008)，该算法可供R社区使用。我们简要介绍了Miwa的程序，并将其与Genz和Bretz(1999)提出的准随机蒙特卡罗程序进行了比较，该程序已通过mvtnorm提供了多年。新算法适用于小于20维的问题，而Genz和Bretz(1999)的程序可用于评估1000维的正态分布。在本文的最后，给出了在不同情况下选择合适算法的建议。

引用次数: 33

Rotating antibiotics in the intensive care unit: feasible, apparently beneficial, but questions remain. 在重症监护室轮流使用抗生素：可行，明显有益，但问题依然存在。

4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal

Pub Date : 2002-02-01 Epub Date: 2002-01-11 DOI: 10.1186/cc1445

Jean-Claude Pechère

引用次数: 0