Description: The high-level language of R is recognized as one of the most powerful
and flexible statistical software environments, and is rapidly becoming
the standard setting for quantitative analysis, statistics and graphics.
R provides free access to unrivalled coverage and cutting-edge
applications, enabling the user to apply numerous statistical methods
ranging from simple regression to time series or multivariate analysis.
Building on the success of the author’s bestselling Statistics: An Introduction using R, The R Book
is packed with worked examples, providing an all inclusive guide to R,
ideal for novice and more accomplished users alike. The book assumes no
background in statistics or computing and introduces the advantages of
the R environment, detailing its applications in a wide range of
disciplines.
- Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities.
- Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test.
- Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more.
The R Book is aimed at undergraduates, postgraduates and
professionals in science, engineering and medicine. It is also ideal for
students and professionals in statistics, economics, geography and the
social sciences.
Contents:
Preface
1 Getting Started
2 Essentials of the R Language
3 Data Input
4 Dataframes
5 Graphics
6 Tables
7 Mathematics
8 Classical Tests
9 Statistical Modelling
10 Regression
11 Analysis of Variance
12 Analysis of Covariance
13 Generalized Linear Models
14 Count Data
15 Count Data in Tables
16 Proportion Data
17 Binary Response Variables
18 Generalized Additive Models
19 Mixed-Effects Models
20 Non-linear Regression
21 Tree Models
22 Time Series Analysis
23 Multivariate Statistics
24 Spatial Statistics
25 Survival Analysis
26 Simulation Models
27 Changing the Look of Graphics
References and Further Reading
Index
2 Essentials of the R Language
3 Data Input
4 Dataframes
5 Graphics
6 Tables
7 Mathematics
8 Classical Tests
9 Statistical Modelling
10 Regression
11 Analysis of Variance
12 Analysis of Covariance
13 Generalized Linear Models
14 Count Data
15 Count Data in Tables
16 Proportion Data
17 Binary Response Variables
18 Generalized Additive Models
19 Mixed-Effects Models
20 Non-linear Regression
21 Tree Models
22 Time Series Analysis
23 Multivariate Statistics
24 Spatial Statistics
25 Survival Analysis
26 Simulation Models
27 Changing the Look of Graphics
References and Further Reading
Index
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