|
|
Introduction ......1
1. Starting Up....3
1.1 Getting started under Windows .........................3
1.2 Use of an Editor Script Window ........................4
1.3 A Short R Session...............5
1.4 Further Notational Details7
1.5 On-line Help.......................7
1.6 The Loading or Attaching of Datasets..............7
1.7 Exercises.............................8
2. An Overview of R........................9
2.1 The Uses of R ..........................9
2.2 R Objects 11
*2.3 Looping.12
2.4 Vectors....12
2.5 Data Frames .........................15
2.6 Common Useful Functions ...16
2.7 Making Tables.......................17
2.8 The Search List .....................17
2.9 Functions in R.......................18
2.10 More Detailed Information .19
2.11 Exercises..............................19
3. Plotting.........21
3.1 plot () and allied functions ...21
3.2 Fine control – Parameter settings .......................21
3.3 Adding points, lines and text .22
3.4 Identification and Location on the Figure Region ............................24
3.5 Plots that show the distribution of data values...25
3.6 Other Useful Plotting Functions.........................27
3.7 Plotting Mathematical Symbols ...........................29
3.8 Guidelines for Graphs ..........29
3.9 Exercises.30
3.10 References ...........................31
4. Lattice graphics .........................33
4.1 Examples that Present Panels of Scatterplots – Using xyplot() 33
4.2 An incomplete list of lattice Functions ................34
4.3 Exercises.35
5. Linear (Multiple Regression) Models and Analysis of Variance......37
5.1 The Model Formula in Straight Line Regression ..............................37
iv
i v
5.2 Regression Objects ...............37
5.3 Model Formulae, and the X Matrix .....................38
5.4 Multiple Linear Regression Models ....................40
5.5 Polynomial and Spline Regression ......................43
5.6 Using Factors in R Models ...46
5.7 Multiple Lines – Different Regression Lines for Different Species ..48
5.8 aov models (Analysis of Variance) ......................49
5.9 Exercises.51
5.10 References ...........................52
6. Multivariate and Tree-based Methods ..................55
6.1 Multivariate EDA, and Principal Components Analysis ..................55
6.2 Cluster Analysis ....................56
6.3 Discriminant Analysis ..........56
6.4 Decision Tree models (Tree-based models)........57
6.5 Exercises.58
6.6 References .............................58
*7. R Data Structures ...................59
7.1 Vectors....59
7.2 Missing Values ......................59
7.3 Data frames...........................60
7.4 Data Entry .............................61
7.5 Factors and Ordered Factors ..............................62
7.6 Ordered Factors ...................63
7.7 Lists.........64
*7.8 Matrices and Arrays ...........64
7.9 Exercises.66
8. Functions .....67
8.1 Functions for Confidence Intervals and Tests ....67
8.2 Matching and Ordering........67
8.3 String Functions....................67
8.4 Application of a Function to the Columns of an Array or Data Frame..........................68
*8.5 aggregate() and tapply() ....68
*8.7 Merging Data Frames ........69
8.8 Dates.......69
8.9. Writing Functions and other Code .....................70
8.10 Exercises..............................73
*9. GLM, and General Non-linear Models ...............75
9.1 A Taxonomy of Extensions to the Linear Model .75
9.2 Logistic Regression...............76
v
v
9.3 glm models (Generalized Linear Regression Modelling) .................79
9.4 Models that Include Smooth Spline Terms..........79
9.5 Survival Analysis...................79
9.6 Non-linear Models ................80
9.7 Model Summaries .................80
9.8 Further Elaborations............80
9.9 Exercises.80
9.10 References ...........................80
*10. Multi-level Models, Repeated Measures and Time Series.............81
10.1 Multi-Level Models, Including Repeated Measures Models...........81
10.2 Time Series Models.............85
10.3 Exercises..............................85
10.4 References ...........................86
*11. Advanced Programming Topics .........................87
11.1. Methods ..............................87
11.2 Extracting Arguments to Functions...................87
11.3 Parsing and Evaluation of Expressions ............88
11.4 Plotting a mathematical expression ..................89
11.4 Searching R functions for a specified token......90
12. Appendix 1 91
12.1 R Packages for Windows....91
12.2 Contributed Documents and Published Literature..........................91
12.3 Data Sets Referred to in these Notes .................92
12.4 Answers to Selected Exercises ...........................92
下载：
Using R for Data Analysis and Graphics Introduction,Code and Commentary.pdf
(2.14 MB, 下载次数: 19, 售价: 5 金钱)
为长期从事生物统计行业的各位提供下载资料。
为了避免恶意下载和滥用，本贴设置了下载积分限制。
生物统计家园提醒您：您在生物统计家园的活动都可能会增加您的积分，使您获得更多权限。
多发些资料供别人下载也可以赚取积分！
如何获得积分（详细）： http://www.biostatistic.net/home.php?mod=spacecp&ac=credit&op=rule
|
|
发表于 2010-12-5 21:54:39
发表于 2011-4-25 20:32:47