LEADER 04569nam 22006735 450 001 9910739406103321 005 20220623181342.0 010 $a3-642-36809-3 024 7 $a10.1007/978-3-642-36809-7 035 $a(CKB)2670000000403423 035 $a(EBL)1317691 035 $a(OCoLC)854976127 035 $a(SSID)ssj0000962491 035 $a(PQKBManifestationID)11491865 035 $a(PQKBTitleCode)TC0000962491 035 $a(PQKBWorkID)10969734 035 $a(PQKB)11178315 035 $a(DE-He213)978-3-642-36809-7 035 $a(MiAaPQ)EBC1317691 035 $a(PPN)172426030 035 $a(EXLCZ)992670000000403423 100 $a20130704d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAnalyzing compositional data with R$b[electronic resource] /$fby K. Gerald van den Boogaart, Raimon Tolosana-Delgado 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (269 p.) 225 1 $aUse R!,$x2197-5736 300 $aDescription based upon print version of record. 311 $a3-642-36808-5 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Fundamental Concepts of Compositional Data Analysis -- Distributions for Random Compositions -- Descriptive Analysis of Compositional Data -- Linear Models for Compositions -- Multivariate Statistics -- Zeroes, Missings and Outliers -- References -- Index.  . 330 $aThis book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package ?compositions,? it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained. 410 0$aUse R!,$x2197-5736 606 $aStatistics  606 $aGeochemistry 606 $aR (Computer program language) 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aGeochemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/G14003 615 0$aStatistics . 615 0$aGeochemistry. 615 0$aR (Computer program language). 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aGeochemistry. 676 $a005.55 700 $avan den Boogaart$b K. Gerald$4aut$4http://id.loc.gov/vocabulary/relators/aut$0521436 702 $aTolosana-Delgado$b Raimon$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910739406103321 996 $aAnalyzing compositional data with R$93552811 997 $aUNINA