國立高雄大學圖資館 |

Language: English

Back

Geostatistics for compositional data...

Mueller, Ute.

Geostatistics for compositional data with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Geostatistics for compositional data with Rby Raimon Tolosana-Delgado, Ute Mueller.
Author:	Tolosana-Delgado, Raimon.
other author:	Mueller, Ute.
Published:	Cham :Springer International Publishing :2021.
Description:	xxv, 259 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	GeologyStatistical methods.
Online resource:	https://doi.org/10.1007/978-3-030-82568-3
ISBN:	9783030825683$q(electronic bk.)

Geostatistics for compositional data with R
Tolosana-Delgado, Raimon.

Geostatistics for compositional data with R[electronic resource] /by Raimon Tolosana-Delgado, Ute Mueller. - Cham :Springer International Publishing :2021. - xxv, 259 p. :ill., digital ;24 cm. - Use R!,2197-5744. - Use R!..

1 Introduction -- 2 A review of compositional data analysis -- 3 Exploratory data analysis -- 4 Exploratory spatial analysis -- 5 Variogram Models -- 6 Geostatistical estimation -- 7 Cross-validation -- 8 Multivariate normal score transformation -- 9 Simulation -- 10 Compositional Direct Sampling Simulation -- 11 Evaluation and Postprocessing of Results -- A Matrix decompositions -- B Complete data analysis workflows -- Index.

This book provides a guided approach to the geostatistical modelling of compositional spatial data. These data are data in proportions, percentages or concentrations distributed in space which exhibit spatial correlation. The book can be divided into four blocks. The first block sets the framework and provides some background on compositional data analysis. Block two introduces compositional exploratory tools for both non-spatial and spatial aspects. Block three covers all necessary facets of multivariate spatial prediction for compositional data: variogram modelling, cokriging and validation. Finally, block four details strategies for simulation of compositional data, including transformations to multivariate normality, Gaussian cosimulation, multipoint simulation of compositional data, and common postprocessing techniques, valid for both Gaussian and multipoint methods. All methods are illustrated via applications to two types of data sets: one a large-scale geochemical survey, comprised of a full suite of geochemical variables, and the other from a mining context, where only the elements of greatest importance are considered. R codes are included for all aspects of the methodology, encapsulated in the R package "gmGeostats", available in CRAN.

ISBN: 9783030825683$q(electronic bk.)

Standard No.: 10.1007/978-3-030-82568-3doiSubjects--Topical Terms:

191971
Geology
--Statistical methods.

LC Class. No.: QE33.2.S82 / T65 2021

Dewey Class. No.: 550.727

Geostatistics for compositional data with R
LDR:02730nmm 22003375a 4500 001 614059
003 DE-He213
005 20211119165159.0
006 m d
007 cr nn 008maaau
008 220627s2021 sz s 0 eng d
020 $a 9783030825683$q(electronic bk.)
020 $a 9783030825676$q(paper)
024 7 $a 10.1007/978-3-030-82568-3 $2 doi
035 $a 978-3-030-82568-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QE33.2.S82 $b T65 2021
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 550.727 $2 23
090 $a QE33.2.S82 $b T653 2021
100 1 $a Tolosana-Delgado, Raimon. $3 715173
245 1 0 $a Geostatistics for compositional data with R $h [electronic resource] / $c by Raimon Tolosana-Delgado, Ute Mueller.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xxv, 259 p. : $b ill., digital ; $c 24 cm.
490 1 $a Use R!, $x 2197-5744
505 0 $a 1 Introduction -- 2 A review of compositional data analysis -- 3 Exploratory data analysis -- 4 Exploratory spatial analysis -- 5 Variogram Models -- 6 Geostatistical estimation -- 7 Cross-validation -- 8 Multivariate normal score transformation -- 9 Simulation -- 10 Compositional Direct Sampling Simulation -- 11 Evaluation and Postprocessing of Results -- A Matrix decompositions -- B Complete data analysis workflows -- Index.
520 $a This book provides a guided approach to the geostatistical modelling of compositional spatial data. These data are data in proportions, percentages or concentrations distributed in space which exhibit spatial correlation. The book can be divided into four blocks. The first block sets the framework and provides some background on compositional data analysis. Block two introduces compositional exploratory tools for both non-spatial and spatial aspects. Block three covers all necessary facets of multivariate spatial prediction for compositional data: variogram modelling, cokriging and validation. Finally, block four details strategies for simulation of compositional data, including transformations to multivariate normality, Gaussian cosimulation, multipoint simulation of compositional data, and common postprocessing techniques, valid for both Gaussian and multipoint methods. All methods are illustrated via applications to two types of data sets: one a large-scale geochemical survey, comprised of a full suite of geochemical variables, and the other from a mining context, where only the elements of greatest importance are considered. R codes are included for all aspects of the methodology, encapsulated in the R package "gmGeostats", available in CRAN.
650 0 $a Geology $x Statistical methods. $3 191971
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
650 2 4 $a Theoretical Ecology/Statistics. $3 276089
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
700 1 $a Mueller, Ute. $3 912207
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Use R!. $3 797602
856 4 0 $u https://doi.org/10.1007/978-3-030-82568-3
950 $a Mathematics and Statistics (SpringerNature-11649)