Chemometrics with R Multivariate Data Analysis in the Natural Scienc
"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. Seve
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Ron Wehrens
Chemometrics with R Multivariate Data Analysis in the Natural Sciences and Life Sciences
Ron Wehrens Fondazione Edmund Mach Research and Innovation Centre Via E. Mach 1 38010 San Michele all’Adige Italy [email protected] Series Editors: Robert Gentleman Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Avenue, N. M2-B876 Seattle, Washington 98109 USA
Kurt Hornik Department of Statistik and Mathematik Wirtschaftsuniversität Wien Augasse 2-6 A-1090 Wien Austria
Giovanni Parmigiani The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 550 North Broadway Baltimore, MD 21205-2011 USA
ISBN 978-3-642-17840-5 e-ISBN 978-3-642-17841-2 DOI 10.1007/978-3-642-17841-2 Springer Heidelberg Dordrecht London New York © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
For Odilia, Chris and Luc
Preface
The natural sciences, and the life sciences in particular, have seen a huge increase in the amount and complexity of data being generated with every experiment. It is only some decades ago that scientists were typically measuring single numbers – weights, extinctions, absorbances – usually directly related to compound concentrations. Data analysis came down to estimating univariate regression lines, uncertainties and reproducibilities. Later, more sophisticated equipment generated complete spectra, where the response of the system is wavelength-dependent. Scientists were confronted with the question how to turn these spectra into useable results such as concentrations. Things became more complex after that: chromatographic techniques for separating mixtures were coupled to high-resolution (mass) spectrometers, yielding a data matrix for every sample, often with large numbers of variables in both chromatographic and spectroscopic directions. A set of such samples corresponds to a data cube rather than a matrix. In parallel, rapid developments in biology saw a massive increase in the rat
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