R IN ACTION第二版

所需积分/C币:10 2015-10-25 15:23:43 14.77MB PDF

R IN ACTION第二版 R in Action Data Analysis and Graphics with R 第二版
References Index List of Figures List of tables List of Listings Table of contents Copyright Brief Table of contents Table of contents Praise for the First edition Preface Acknowledgments about this book about the cover lllustration . Getting started Chapter 1 Introduction to R 1. 1. Why use R? 1.2. Obtaining and installing R 13. Working with r 1.3.1. Getting started 1.3.2. Getting help 1.3.3. The workspace 1.3.4. Input and output 1. 4. Packages 1.4.1. What are packages? 1.4.2. Installing a package 1.4.3. Loading a package 1.4.4. Learning about a package 1.5. Batch processing 1.6. Using output as input: reusing results 1.7. Working with large datasets 1. 8. Working through an example 1.9. Summary Chapter 2. Creating a dataset 2.1. Understanding datasets 2.2. Data structures 2.2.1. Vectors 2.2.2. Matrices 2.2.3.Aays 2.2.4. Data frames 2.2.5. Factors 2.2.6. Lists 2.3. Data input 2.3.1. Entering data from the keyboard 2.3.2. Importing data from a delimited text file 2.3.3. Importing data from Excel 2.3.4. Importing data from XML 2.3.5. Importing data from the web 2.3.6. Importing data from SPSs 2.3.7. Importing data from SAs 2.3.8. Importing data from Stata 2.3.9. Importing data from Net CDF 2.3.10. Impor ting data from HDF5 2.3.11. Accessing database manag ement systems (DBMSs 2.3.12. Importing data via Stat/Transfer 2.4. Annotating datasets 241.Variable labels 242. Value labels 2.5. Useful functions for working with data objects 2.6. Summary Chapter 3. Getting started with graphs 3.1. Working with graphs 3.2. A simple example 3.3. Graphical parameters 3.3.1. Symbols and lines 3.3.2 Colors 333. Text characteristics 3.3.4. Graph and mar gin dimensions 3.4. Adding text, customized axes, and legends 3. 4.1. Titles 3.4.2.Axes 3.4.3. Reference lines 3.4.4. Legend 3.45. Text annotations 3.4.6. Math annotations 3.5. Combining graphs 3.5.1. Creating a figure arrangement with fine control 3.6. Summary Chapter 4 Basic data management 4.1. A working example 4.2. Creating new variables 4.3. Recoding variables 4.4. Renaming variables 4.5. Missing values 4.5.1. Recoding values to missing 4.5.2. Excluding missing values from analyses 46. Date values 4.6.1. Converting dates to character variables 4.6.2. Going further 4.7. Type conversions 4.8. Sorting data 4.9. Merging datasets 4.9.1. Adding columns to a data frame 4.9.2. Adding rows to a data frame 4.10. Subsetting datasets 4.10.1. Selecting(keeping )variables 4.10.2. Excluding (dropping variables 4.10.3. Selecting observations 4.10.4. The subset function 4.10.5. Random samples 4. 11. Using SQL statements to manipulate data frames 4.12. Summary Chapter 5. Advanced data management 5. 1. a data-management challenge 5.2. Numerical and character functions 5.2.1. Mathematical functions 5. 2.2. Statistical functions 5.2.3. Probability functions 5.2.4. Character functions 5.2.5. Other useful functions 5.2.6. Applying functions to matrices and data frames 5.3. A solution for the data-management challenge 5.4. Control flow 5.4.1. Repetition and looping 5. 4.2. Conditional execution 5.5. User-written functions 5.6. Aggregation and reshaping 5.6.1. Transpose 5.6.2. Aggregating data 5.6.3. The reshape package 5.7. Summar 2. Basic methods Chapter 6. Basic graphs 6.1. Bar plots 6.1.1. Simple bar plots 6.1.2. Stacked and grouped bar plots 6.1.3. Mean bar plots 6.1.4. Tweaking bar plots 6.1.5. Spinograms 6.2 Pie charts 6.3. Histograms 6.4. Kernel density plots 6.5. Box plots 6.5.1. Using parallel box plots to compare groups 6.5.2. Violin plots 6.6. Dot plots 6.7. Summary Chapter 7. Basic statistics 7.l. Descriptive statistics 7.1.1. A menagerie of methods 712. Even more methods 7.1.3. Descriptive statistics by group 7.1.4. Additional methods by group 7.1.5. Visualizing results 72. Frequency and contingency tables 7.2.1. Generating frequency tables 7.2.2. Tests of independence 7. 2.3. Measures of association 7. 4. visualizing results 73. Correlations 7.3. 1. Types of correlations 7.3. 2. Testing correlations for significance 7.3.3. Visualizing correlations 74. -tests 7.4.1. Independent t-test 7.4.2. Dependent t-test 7.4.3. When there are more than two groups 7.5. Nonparametric tests of group differences 7.5. 1. Comparing two groups 7.5.2. Comparing more than two groups 7.6. Visualizing group differences 7.7. Summary 3. Intermediate methods Chapter 8. Regression 8.1. The many faces of regression 8.1.1. Scenarios for using OLs regression 8.1.2. What you need to know 8.2. OLS regression 8.2.1. Fitting regression models with ImO 8.2.2. Simple linear regression 8.2.3. Polynomial regression 8. 2.4. Multiple linear regression 8.2.5. Multiple linear regression with interactions 8.3. Regression diagnostics 8.3.1. A typical approach 8.3.2. An enhanced approach 8.3.3. Global validation of linear model assumption 8.3.4. Multicollinearity 8. 4. Unusual observations 8. 41. Outliers 8. 4.2. High-leverage points 8.4.3. Influential observations 8.5. Corrective measures 8.5.1. Deleting observations 8.5.2. Transforming variables 8.5.3. Adding or deleting variables 8.5.4. Trying a different approach 8.6. Selecting the "best regression model 8.6. 1. Comparing models 8.6.2 Variable selection 8.7. T aking the analysis further 8.7.1. Cross-validation 8.7.2. Relative importance 8.8. Summary Chapter 9. Analysis of variance 9.1. A crash course on terminology 9.2. Fitting ANOVA models 9. 2. 1. The aov function 9.2.2. The order of formula terms 9.3. One-way ANOVA 9.3.1. Multiple comparisons 9.3.2. Assessing test assumptions 9.4. One-way ANCOVA 9.4.1. Assessing test assumptions 9.4.2. Visualizing the results 9.5. Two-way factorial ANOVA 9.6. Repeated measures anova 9.7. Multivariate analysis of variance ( MaNOva 9.7.1. Assessing test assumptions 9.7.2. Robust manova 9.8. ANOVa as regression 9. 9. Summary Chapter 10. Power analysis 10.1. A quick review of hypothesis testing 10.2. Implementing power analysis with the pwr package 10.2 1. t-tests 10.2.2 ANOVA 10.23. Correlations 10.24. Linear models 10.2.5.Tests of proportions 10.2.6. Chi-square tests 10.2.7. Choosing an appropriate effect size in novel situations 10.3. Creating power analysis plots 10.4. Other packages 10.5. Summary Chapter 11. Intermediate graphs 11.1. Scatter plots 11.1.1. Scatter-plot matrices 111.2. High-density scatter plots 11. 1.3. 3D scatter plots 11.1.4. Spinning 3D scatter plots 11.1.5. Bubble plots 112. Line charts 11.3. Corrgrams 11.4. Mosaic plots 11.5. Summary Chapter 12. Resampling statistics and bootstrapping 21. Permutation tests 2.2. Permutation tests with the coin package 12.2.1. Independent two-sample and k-sample tests 12.2.2. Independence in contingency tables 12.2.3. Independence between numeric variables 12.2.4. Dependent two-sample and k-sample tests 12.2.5. Going fur ther 123 Permutation tests with the ImPerm package 123.1. Simple and polynomial regression 123. 2. Multiple regression 123.3 One-way ANOVa and anCOVA 123.4. TWo-way ANOVA 12.4. Additional comments on permutation tests 12.5. Bootstrapping 12.6. Bootstrapping with the boot package 12.6.1. Bootstr apping a single statistic 12.6.2. Bootstrapping several statistics 127. Summar 4. Advanced methods Chapter 13. Generalized linear models 13.1. Generalized linear models and the gmo function 13.11. The gImO function 13.1.2. Supporting functions 13.1.3. Model fit and regression diagnostics 13.2. Logistic regression 13.]. Interpreting the model parameters 13.2.2. Assessing the impact of predictors on the probability of an outcome 13.2.3. Overdispersion 13.24. Extensions 13.3. Poisson regression 13.3. 1 Interpreting the model parameters 13.3.2. Overdispersion 133.3. Extensions 13.4. Summary Chapter 14 Principal components and factor analysis 14.1. Principal components and factor analysis in R 14.2. Principal components 14.2.1. Selecting the number of components to extract 14.2.2. EXtracting principal components 14.2.3. Rotating principal components 14.2.4. Obtaining principal components scores 143. EXploratory factor analysis 143.1. Deciding how many common factors to extract 143.2. Extracting common factors 143.3. Rotating factors 1434. Factor scores 14.3.5. Other EFA-related packages 14.4. Other latent variable models 14.5. Summary Chapter 15. Time series 15.1. Creating a time-series object in R 5.2. Smoothing and seasonal decomposition 15.2.1. Smoothing with simple moving averages 15.2.2. Seasonal decomposition 15.3. Exponential forecasting models 153.1. Simple exponential smoothing 15.3. 2 Holt and Holt-Winters exponential smoothing 15.3.3. The etso function and automated forecasting

...展开详情

评论 下载该资源后可以进行评论 2

xwy5201314 不是扫描版的,可以看哦。
2018-09-12
回复
zhuxianyuan2007 不错的R学习书
2018-07-16
回复
img
ature6

关注 私信 TA的资源

上传资源赚积分,得勋章
    最新推荐