The R BookThe 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.
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. |
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Contents
vii | |
1 | |
9 | |
3 Data Input | 97 |
4 Dataframes | 107 |
5 Graphics | 135 |
6 Tables | 183 |
7 Mathematics | 195 |
16 Proportion Data | 569 |
17 Binary Response Variables | 593 |
18 Generalized Additive Models | 611 |
19 MixedEffects Models | 627 |
20 Nonlinear Regression | 661 |
21 Tree Models | 685 |
22 Time Series Analysis | 701 |
23 Multivariate Statistics | 731 |
8 Classical Tests | 279 |
9 Statistical Modelling | 323 |
10 Regression | 387 |
11 Analysis of Variance | 449 |
12 Analysis of Covariance | 489 |
13 Generalized Linear Models | 511 |
14 Count Data | 527 |
15 Count Data in Tables | 549 |
24 Spatial Statistics | 749 |
25 Survival Analysis | 787 |
26 Simulation Models | 811 |
27 Changing the Look of Graphics | 827 |
873 | |
877 | |
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Common terms and phrases
analysis of variance argument axis bootstrap BrandD calculate called categorical chi-squared Clone1 Coefficients colour column confidence interval containing continuous explanatory variables contrasts correlation count data covariance curve data points dataframe default degrees of freedom density Df Deviance Df Sum Error t value Estimate Std Estimate Std.Error example explanatory variables F-statistic factor levels FALSE female fitted values fixed effects frequencies function grahamii graph Grassland individuals Intercept labels linear model male matrix mean median minimal adequate model mixed-effects model model formula model simplification multiple neighbours non-linear normal distribution null hypothesis overdispersion p-value plot Poisson population probability proportion pseudoreplication R-squared random effects random numbers regression replicates Residual deviance Residual standard error response variable sample scale scatterplot Scrub shows significant significantly different slope Soil.pH spatial species specify standard deviation standard error statistical subscripts sum of squares summary(model tannin treatment TRUE value Pr(>|t vector zero