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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
R Journal Pub Date : 2013-06-01
Lee S McDaniel, Nicholas C Henderson, Paul J Rathouz
{"title":"Fast Pure R Implementation of GEE: Application of the Matrix Package.","authors":"Lee S McDaniel,&nbsp;Nicholas C Henderson,&nbsp;Paul J Rathouz","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, <b>geeM</b>, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the <b>Matrix</b> 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 <b>geeM</b>, which is not much worse than C implementations like <b>geepack</b> and <b>gee</b> on small data sets and faster on large data sets.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289620/pdf/nihms-607237.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32974591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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
R Journal Pub Date : 2011-12-01 DOI: 10.32614/RJ-2011-015
Haizhou Wang, Mingzhou Song
{"title":"Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming","authors":"Haizhou Wang, Mingzhou Song","doi":"10.32614/RJ-2011-015","DOIUrl":"https://doi.org/10.32614/RJ-2011-015","url":null,"abstract":"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.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
R Journal Pub Date : 2011-12-01
Haizhou Wang, Mingzhou Song
{"title":"Ckmeans.1d.dp: Optimal <i>k</i>-means Clustering in One Dimension by Dynamic Programming.","authors":"Haizhou Wang, Mingzhou Song","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The heuristic <i>k</i>-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 <b>Ckmeans.1d.dp</b>. We demonstrate its advantage in optimality and runtime over the standard iterative <i>k</i>-means algorithm.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5148156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
R Journal Pub Date : 2010-12-01 DOI: 10.32614/RJ-2010-016
C. Bilder, Boan Zhang, F. Schaarschmidt, J. Tebbs
{"title":"binGroup: A Package for Group Testing","authors":"C. Bilder, Boan Zhang, F. Schaarschmidt, J. Tebbs","doi":"10.32614/RJ-2010-016","DOIUrl":"https://doi.org/10.32614/RJ-2010-016","url":null,"abstract":"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.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
R Journal Pub Date : 2010-12-01
Christopher R Bilder, Boan Zhang, Frank Schaarschmidt, Joshua M Tebbs
{"title":"binGroup: A Package for Group Testing.","authors":"Christopher R Bilder,&nbsp;Boan Zhang,&nbsp;Frank Schaarschmidt,&nbsp;Joshua M Tebbs","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3152446/pdf/nihms267443.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29925479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
R Journal Pub Date : 2009-01-01 DOI: 10.15488/3835
Xuefei Mi, Tetsuhisa Miwa, T. Hothorn
{"title":"mvtnorm: New numerical algorithm for multivariate normal probabilities","authors":"Xuefei Mi, Tetsuhisa Miwa, T. Hothorn","doi":"10.15488/3835","DOIUrl":"https://doi.org/10.15488/3835","url":null,"abstract":"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.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77916968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 33
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