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。
我也不想为了避免重复把四个帖子的标题都改了，不然有诱骗别人重复下载的嫌疑，所以就这么挂着吧。
抱歉啦。

嘿嘿，没事，关键是抢我生意，，

哈哈，抱歉啦，日后有机会补偿给你吧。