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Chemometrics with R : Multivariate Data Analysis in the Natural Sciences and Life Sciences / by Ron Wehrens.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Use REditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: xiv, 285 páginas 99 ilustraciones recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783642178412
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • QH324.2-324.25
Recursos en línea:
Contenidos:
Introduction -- Part I Preliminaries: Data -- Preprocessing -- Part II Exploratory Analysis: Principal Component Analysis -- Self-Organizing Maps -- Clustering -- Part III Modelling: Classification -- Multivariate Regression -- Part IV Model Inspection: Validation -- Variable Selection -- Part V Applications: Chemometric -- Part VI Appendices: R packages Used in This Book -- References -- Index.
Resumen: "Chemometrics with R" offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a complete description of the general data analysis paradigm, from exploratory analysis to modeling to validation. Several more specific topics from the area of chemometrics are included in a special section. The corresponding R code is provided for all the examples in the book; scripts, functions and data are available in a separate, publicly available R package. For researchers working in the life sciences, the book can also serve as an easy-to-use primer on R.
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Springer eBooks

Introduction -- Part I Preliminaries: Data -- Preprocessing -- Part II Exploratory Analysis: Principal Component Analysis -- Self-Organizing Maps -- Clustering -- Part III Modelling: Classification -- Multivariate Regression -- Part IV Model Inspection: Validation -- Variable Selection -- Part V Applications: Chemometric -- Part VI Appendices: R packages Used in This Book -- References -- Index.

"Chemometrics with R" offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a complete description of the general data analysis paradigm, from exploratory analysis to modeling to validation. Several more specific topics from the area of chemometrics are included in a special section. The corresponding R code is provided for all the examples in the book; scripts, functions and data are available in a separate, publicly available R package. For researchers working in the life sciences, the book can also serve as an easy-to-use primer on R.

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