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
bill_law62012-06-12好东西，R语言太好使了。
