Forest Analytics with R

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 statistical and mathematical tools are introduc...

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Những tác giả chính:	Robinson, Andrew P., Hamann, Jeff D.
Định dạng:	Sách
Ngôn ngữ:	English
Được phát hành:	Springer 2012
Những chủ đề:	Forestry forest biometrics forest informatics forest management
Truy cập trực tuyến:	http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/29731
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id	oai:scholar.dlu.edu.vn:DLU123456789-29731
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spelling	oai:scholar.dlu.edu.vn:DLU123456789-297312012-03-03T07:27:57Z Forest Analytics with R Robinson, Andrew P. Hamann, Jeff D. Forestry forest biometrics forest informatics forest management 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 statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others. 2012-02-08T03:35:17Z 2012-02-08T03:35:17Z 2011 Book 978-1-4419-7761-8 978-1-4419-7762-5 http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/29731 en application/pdf Springer
institution	Thư viện Trường Đại học Đà Lạt
collection	Thư viện số
language	English
topic	Forestry forest biometrics forest informatics forest management
spellingShingle	Forestry forest biometrics forest informatics forest management Robinson, Andrew P. Hamann, Jeff D. Forest Analytics with R
description	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 statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others.
format	Book
author	Robinson, Andrew P. Hamann, Jeff D.
author_facet	Robinson, Andrew P. Hamann, Jeff D.
author_sort	Robinson, Andrew P.
title	Forest Analytics with R
title_short	Forest Analytics with R
title_full	Forest Analytics with R
title_fullStr	Forest Analytics with R
title_full_unstemmed	Forest Analytics with R
title_sort	forest analytics with r
publisher	Springer
publishDate	2012
url	http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/29731
_version_	1757669863863615488

Forest Analytics with R

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