2011R新书四册之四：Forest Analytics with R -- An Introduction

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Forest Analytics with R: An Introduction (Use R)
Springer; 1st Edition. edition (December 14, 2010) | ISBN: 1441977619 | 354 pages | PDF | 3 MB

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.

Forest Analytics with R.rar (2.7 MB, 需要: 3 个论坛币) 本附件包括：

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2010-11-17 12:41:41 上传

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四册图书地址汇总：


2011R新书四册之一：The Foundations of Statistics:A Simulation-based Approach
http://www.pinggu.org/bbs/thread-963141-1-1.html

2011R新书四册之二：第三版Time Series Analysis and Applications with R examples
http://www.pinggu.org/bbs/thread-963621-1-1.html

2011R新书四册之三：Statistics and Data Analysis for Financial Engineering
http://www.pinggu.org/bbs/thread-963642-1-1.html

2011R新书四册之四：Forest Analytics with R -- An Introduction
http://www.pinggu.org/bbs/thread-963673-1-1.html

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关键词：introduction troduction Analytics Analytic Forest 新书 introduction Analytics Forest

1# yizhengchina
Preface . vii
Part I Introduction and Data Management
1 Introduction . 3
1.1 This Book . 5
1.1.1 Topics Covered in This Book . 5
1.1.2 Conventions Used in This Book . 6
1.1.3 The Production of the Book . 6
1.2 Software . 7
1.2.1 Communicating with R . 7
1.2.2 Getting Help . 9
1.2.3 Using Scripts . 11
1.2.4 Extending R . 12
1.2.5 Programming Suggestions . 13
1.2.6 Programming Conventions. 14
1.2.7 Speaking Other Languages . 15
1.3 Notes about Data Analysis . 16
2 Forest Data Management . 19
2.1 Basic Concepts . 19
2.2 File Functions. 20
2.2.1 Text Files . 20
2.2.2 Spreadsheets . 25
2.2.3 Using SQL in R . 26
2.2.4 The foreign Package . 26
2.2.5 Geographic Data . 28
2.2.6 Other Data Formats . 29
2.3 Data Management Functions . 30
2.3.1 Herbicide Trial Data . 31
2.3.2 Simple Error Checking . 33
2.3.3 Graphical error checking . 34
2.3.4 Data Structure Functions. 37
2.4 Examples. 43
2.4.1 Upper Flat Creek in the UIEF . 43
2.4.2 Sweetgum Stem Profiles . 46
2.4.3 FIA Data . 51
2.4.4 Norway Spruce Profiles . 52
2.4.5 Grand Fir Profiles . 53
2.4.6 McDonald–Dunn Research Forest . 55
2.4.7 Priest River Experimental Forest . 61
2.4.8 Leuschner . 72
2.5 Summary. 72
Part II Sampling and Mapping
3 Data Analysis for Common Inventory Methods . 75
3.1 Introduction . 75
3.1.1 Infrastructure . 76
3.1.2 Example Datasets . 77
3.2 Estimate Computation . 77
3.2.1 Sampling Distribution . 77
3.2.2 Intervals from Large-Sample Theory . 80
3.2.3 Intervals from Linearization . 81
3.2.4 Intervals from the Jackknife . 82
3.2.5 Intervals from the Bootstrap . 84
3.2.6 A Simulation Study . 92
3.3 Single-Level Sampling . 94
3.3.1 Simple Random Sampling . 94
3.3.2 Systematic Sampling. 96
3.4 Hierarchical Sampling . 97
3.4.1 Cluster Sampling . 97
3.4.2 Two-Stage Sampling . 100
3.5 Using Auxiliary Information . 104
3.5.1 Stratified Sampling . 104
3.5.2 Ratio Estimation . 106
3.5.3 Regression Estimation . 109
3.5.4 3P Sampling . 111
3.5.5 VBAR . 113
3.6 Summary. 114
4 Imputation and Interpolation . 117
4.1 Introduction . 117
4.2 Imputation . 118
4.2.1 Examining Missingness Patterns . 118
4.2.2 Methods for Imputing Missing Data . 125
4.2.3 Nearest-Neighbor Imputation . 126
4.2.4 Expectation-Maximization Imputation . 131
4.2.5 Comparing Results . 133
4.3 Interpolation. 135
4.3.1 Methods of Interpolation . 136
4.3.2 Ordinary Kriging . 138
4.3.3 Semi-variogram Estimation . 141
4.3.4 Prediction . 145
4.4 Summary. 148
Part III Allometry and Fitting Models
5 Fitting Dimensional Distributions . 155
5.1 Diameter Distribution. 156
5.2 Non-parametric Representation . 157
5.3 Parametric Representation. 158
5.3.1 Parameter Estimation. 158
5.3.2 Some Models of Choice . 164
5.3.3 Profiling . 168
5.3.4 Sampling Weights . 172
6 Linear and Non-linear Modeling . 175
6.1 Linear Regression . 175
6.1.1 Example . 178
6.1.2 Thinking about the Problem. 180
6.1.3 Fitting the Model . 180
6.1.4 Assumptions and Diagnostics . 181
6.1.5 Examining the Model . 185
6.1.6 Using the Model . 188
6.1.7 Testing Effects . 192
6.1.8 Transformations . 195
6.1.9 Weights . 195
6.1.10 Generalized Least-Squares Models . 197
6.2 Non-linear Regression . 199
6.2.1 Example . 200
6.2.2 Thinking about the Problem. 200
6.2.3 Fitting the Model . 202
6.2.4 Assumptions and Diagnostics . 203
6.2.5 Examining the Model . 207
6.2.6 Using the Model . 210
6.2.7 Testing Effects . 210
6.2.8 Generalized Non-linear Least-Squares Models . 212
6.2.9 Self-starting Functions . 213
6.3 Back to Maximum Likelihood . 214
6.3.1 Linear Regression . 215
6.3.2 Non-linear Regression . 216
6.3.3 Heavy-Tailed Residuals . 217
7 Fitting Linear Hierarchical Models . 219
7.1 Introduction . 219
7.1.1 Effects . 220
7.1.2 Model Construction . 223
7.1.3 Solving a Dilemma . 225
7.1.4 Decomposition . 226
7.2 Linear Mixed-Effects Models . 227
7.2.1 A Simple Example. 227
7.3 Case Study: Height and Diameter Model . 233
7.3.1 Height vs. Diameter . 234
7.3.2 Use More Data. 243
7.3.3 Adding Fixed Effects . 254
7.3.4 The Model . 256
7.4 Model Wrangling . 259
7.4.1 Monitor . 260
7.4.2 Meddle . 260
7.4.3 Modify. 260
7.4.4 Compromise . 261
7.5 The Deep End . 261
7.5.1 Maximum Likelihood . 262
7.5.2 Restricted Maximum Likelihood. 263
7.6 Non-linear Mixed-Effects Models . 264
7.6.1 Hierarchical Approach . 269
7.7 Further Reading. 273
Part IV Simulation and Optimization
8 Simulations. 277
8.1 Generating Simulations . 278
8.1.1 Simulating Young Stands . 279
8.1.2 Simulating Established Stands . 284
8.2 Generating Volumes . 290
8.2.1 The Taper Function . 291
8.2.2 Computing Merchantable Height . 291
8.2.3 Summarizing Log Volumes by Grade . 293
8.2.4 Young-Stand Volumes. 295
8.2.5 Established-Stand Volumes . 296
8.3 Merging Yield Streams . 299
8.4 Examining Results. 299
8.4.1 Volume Distribution . 302
8.4.2 Mean Annual Increment. 304
8.5 Exporting Yields . 305
8.6 Summary. 305
9 Forest Estate Planning and Optimization . 307
9.1 Introduction . 307
9.2 Problem Formulation . 308
9.3 Strict Area Harvest Schedule. 309
9.3.1 Objective Function . 310
9.3.2 Adding Columns . 310
9.3.3 Naming Columns . 311
9.3.4 Bounding Columns . 312
9.3.5 Setting Objective Coefficients . 312
9.3.6 Adding Constraints . 312
9.3.7 Solving . 316
9.3.8 Results . 317
9.3.9 Archiving Problems . 322
9.3.10 Cleanup . 322
9.4 Summary. 323
References . 325
Index . 335

大哥，这个发过啦

昨天搜论坛的时候还没有，没想到今天就有了。
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抱歉啦。

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哈哈，抱歉啦，日后有机会补偿给你吧。

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谢谢啦谢谢啦

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