Forest Analytics with R An Introduction
Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which st
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Kurt Hornik
Giovanni Parmigiani
For a complete list of titles published in this series, go to www.springer.com/series/6991
Use R! Albert: Bayesian Computation with R Bivand/Pebesma/Gomez-Rubio: Applied Spatial Data Analysis with R ´ Claude:Morphometrics with R Cook/Swayne: Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Nason: Wavelet Methods in Statistics with R Paradis: Analysis of Phylogenetics and Evolution with R Peng/Dominici: Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health Pfaff: Analysis of Integrated and Cointegrated Time Series with R, 2nd edition Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R
Andrew P. Robinson • Jeff D. Hamann
Forest Analytics with R An Introduction
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Andrew P. Robinson Dept. Mathematics and Statistics University of Melbourne Parkville 3010 VIC Australia [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
Jeff D. Hamann Forest Informatics, Inc. PO Box 1421 97339-1421 Corvallis Oregon USA [email protected]
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 978-1-4419-7761-8 e-ISBN 978-1-4419-7762-5 DOI 10.1007/978-1-4419-7762-5 Springer New York Dordrecht Heidelberg London © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
This book is dedicated to Grace, Felix, and M’Liss, (and Henry and Yohan) with grateful thanks for inspiration and patience.
Preface
R is an open-source and free software environment for statistical computing and graphics. R compiles and runs on a wide variety of UNIX platforms (e.g., GNU/Linux and FreeBSD), Windows, and Mac OSX. Since the late 1990s, R has been developed by hundreds of contributors and new capabilities are added each month. The software is gaining popularity becaus
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