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New York : Springer, 2012

xiii, 359 s. : il. ; 24 cm

ISBN 978-1-4614-1364-6 (brož.)

Use R!

Obsahuje bibliografii na s. 349-351 a rejstřík

000246915

Contents // 1 Introduction... 1 // 1.1 Getting Started... 1 // 1.1.1 Preliminaries ... 2 // 1.1.2 Basic operations ... 3 // 1.1.3 R Scripts... 11 // 1.1.4 The R Help System... 13 // 1.2 Functions ... 14 // 1.3 Vectors and Matrices ... 16 // 1.4 Data Frames... 19 // 1.4.1 Introduction to data frames... 20 // 1.4.2 Working with a data frame... 22 // 1.5 Importing Data... 26 // 1.5.1 Entering data manually... 26 // 1.5.2 Importing data from a text file... 28 // 1.5.3 Data available on the internet... 30 // 1.6 Packages... 33 // 1.7 The R Workspace... 35 // 1.8 Options and Resources... 36 // 1.9 Reports and Reproducible Research... 39 // Exercises ... 39 // 2 Quantitative Data... 43 // 2.1 Introduction... 43 // 2.2 Bivariate Data: Two Quantitative Variables... 43 // 2.2.1 Exploring the data... 44 // 2.2.2 Correlation and regression line... 46 // 2.2.3 Analysis of bivariate data by group... 48 // 2.2.4 Conditional plots... 50 // 2.3 Multivariate Data: Several Quantitative Variables... 52 // 2.3.1 Exploring the data... 53 // 2.3.2 Missing values... 54 // vii // viii Contents // 2.3.3 Summarize by group... 54 // 2.3.4 Summarize pairs of variables... 55 // 2.3.5 Identifying missing values ... 5g // 2.4 Time Series Data... 59 // 2.5 Integer Data: Draft Lottery... 01 // 2.6 Sample Means and the Central Limit Theorem... 65 // 2.7 Special Topics... 03 // 2.7.1 Adding a new variable... 03 // 2.7.2 Which observation is the maximum?... 09 // 2.7.3 Sorting a data frame... 70 // 2.7.4 Distances

between points... 71 // 2.7.5 Quick look at cluster analysis... 73 // Exercises ... 75 // 3 Categorical data... 79 // 3.1 Introduction... 79 // 3.1.1 Tabulating and plotting categorical data... 79 // 3.1.2 Character vectors and factors... 80 // 3.2 Chi-square Goodness-of-Fit Test... 82 // 3.3 Relating Two Categorical Variables... 85 // 3.3.1 Introduction... 85 // 3.3.2 Frequency tables and graphs... 86 // 3.3.3 Contingency tables... 88 // 3.4 Association Patterns in Contingency Tables... 90 // 3.4.1 Constructing a contingency table... 90 // 3.4.2 Graphing patterns of association... 92 // 3.5 Testing Independence by a Chi-square Test... 93 // Exercises ... 96 // 4 Presentation Graphics...101 // 4.1 Introduction...101 // 4.2 Labeling the Axes and Adding a Title...102 // 4.3 Changing the Plot Type and Plotting Symbol...103 // 4.4 Overlaying Lines and Line Types...105 // 4.5 Using Different Colors for Points and Lines...108 // 4.6 Changing the Format of Text...110 // 4.7 Interacting with the Graph...Ill // 4.8 Multiple Figures in a Window...112 // 4.9 Overlaying a Curve and Adding a Mathematical Expression . 113 // 4.10 Multiple Plots and Varying the Graphical Parameters...116 // 4.11 Creating a Plot using Low-Level Functions ...119 // 4.12 Exporting a Graph to a Graphics File...120 // 4.13 The lattice Package...121 // 4.14 The ggplot2 Package...126 // Exercises ...127 // ix // Contents // 5 Exploratory Data Analysis...133 // 5.1 Introduction...133 // 5 2 Meet the Data...134 // 5

3 Comparing Distributions...135 // 5.3.1 Stripcharts...135 // 5.3.2 Identifying outliers ...136 // 5 3.3 Five-number summaries and boxplots...137 // 5.4 Relationships Between Variables...139 // 5.4.1 Scatterplot and a resistant line...139 // 5.4.2 Plotting residuals and identifying outliers...140 // 5.5 Time Series Data...141 // 5.5.1 Scatterplot, least-squares line, and residuals...141 // 5.5.2 Transforming by a logarithm and fitting a line...143 // 5.6 Exploring Fraction Data...145 // 5.6.1 Stempio!...145 // 5.6.2 Transforming fraction data...146 // Exercises ...149 // 6 Basic Inference Methods...153 // 6.1 Introduction...153 // 6.2 Learning About a Proportion...154 // 6.2.1 Testing and estimation problems...154 // 6.2.2 Creating group variables by the if else function...154 // 6.2.3 Large-sample test and estimation methods...154 // 6.2.4 Small sample methods...156 // 6.3 Learning About a Mean...158 // 6.3.1 Introduction...158 // 6.3.2 One-sample t statistic methods...158 // 6.3.3 Nonparametrie methods...161 // 6.4 Two Sample Inference...163 // 6.4.1 Introduction...163 // 6.4.2 Two sample t-test...163 // 6.4.3 Two sample Mann-Whitney-Wilcoxon test...165 // 6.4.4 Permutation test...166 // 6.5 Paired Sample Inference Using a t Statistic...167 // Exercises ...170 // 7 Regression...173 // 7.1 Introduction...173 // 7.2 Simple Linear Regression...174 // 7.2.1 Fitting the model ...174 // 7.2.2 Residuals...176 // 7.2.3 Regression through the origin...177 // 7.3 Regression Analysis for

Data with Two Predictors...178 // 7.3.1 Preliminary analysis...178 // Contents // 7.3.2 Multiple regression model... // 7.3.3 The summary and anova methods for 1m...? // 7.3.4 Interval estimates for new observations... // 7.4 Fitting a Regression Curve... 87 // Exercises ... ? // 8 Analysis of Variance I...? // 8.1 Introduction... 299 // 8.1.1 Data entry for one-way ANOVA...200 // 8.1.2 Preliminary data analysis ...202 // 8.2 One-way ANOVA ...9q // 8.2.1 ANOVA F test using oneway.test...204 // 8.2.2 One-way ANOVA model ...205 // 8.2.3 ANOVA using 1m or aov...206 // 8.2.4 Fitting the model ...207 // 8.2.5 Tables of means or estimated effects... 207 // 8.2.6 ANOVA Table...208 // 8.3 Comparison of Treatment Means...210 // 8.3.1 Fisher Least Significant Difference (LSD)...210 // 8.3.2 Tukey’s multiple comparison method ...212 // 8.4 A Statistical Reference Dataset from NIST...215 // 8.5 Stacking Data...217 // Exercises ...222 // 8.6 Chapter 8 Appendix: Exploring ANOVA calculations...223 // 9 Analysis of Variance II...227 // 9.1 Introduction...227 // 9.2 Randomized Block Designs...227 // 9.2.1 The randomized block model ...229 // 9.2.2 Analysis of the randomized block model...230 // 9.3 Two-way ANOVA...234 // 9.3.1 The two-way ANOVA model...235 // 9.3.2 Analysis of the two-way ANOVA model...236 // Exercises ...240 // 10 Randomization Tests...243 // 10.1 Introduction...243 // 10.2 Exploring Data for One-way Analysis...243 // 10.3 Randomization Test for Location...246 // 10.4 Permutation

Test for Correlation...249 // Exercises ...252 // Contents // 11 // Simulation Experiments...255 // ? 1 Introduction... // 11 2 Simulating a Game of Chance...255 // 11 2.1 The sample function...256 // 11 2.2 Exploring cumulative winnings...256 // 11 2 3 R function to implement a Monte Carlo experiment.. . 258 // 11.2.4 Summarizing the Monte Carlo results...258 // 112 5 Modifying the experiment to learn about new statistics 260 // ? ? Random Permutations...262 // 11 3.1 Using sample to simulate an experiment...262 // 11.3.2 Comparing two permutations of a sample...263 // 11.3.3 Writing a function to perform simulation...263 // 11.3.4 Repeating the simulation...264 // 11.4 The Collector’s Problem...265 // 11.4.1 Simulating experiment using the sample function...266 // 11.4.2 Writing a function to perform the simulation ...267 // 11.4.3 Buying an optimal number of cards...268 // 11.5 Patterns of Dependence in a Sequence...270 // 11.5.1 Writing a function to compute streaks...270 // 11.5.2 Writing a function to simulate hitting data...271 // Exercises ...273 // 12 Bayesian Modeling ...277 // 12.1 Introduction...277 // 12.2 Learning about a Poisson Rate...278 // 12.3 A Prior Density...278 // 12.4 Information Contained in the Data: the Likelihood ...279 // 12.5 The Posterior and Inferences...281 // 12.5.1 Computation of the posterior...281 // 12.5.2 Exact summarization of the posterior...282 // 12.5.3 Summarizing a posterior by simulation...284 // 12.6 Simulating a Probability Distribution

by a Random Walk . . . 285 // 12.6.1 Introduction...285 // 12.6.2 The Metropolis-Hastings random walk algorithm...286 // 12.6.3 Using an alternative prior...289 // 12.7 Bayesian Model Checking ...290 // 12.7.1 The predictive distribution...291 // 12.7.2 Model checking...292 // 12.8 Negative Binomial Modeling...295 // 12.8.1 Overdispersion...295 // 12.8.2 Fitting the Negative Binomial model...296 // Exercises ...302 // Xll // Contents // 13 // Monte Carlo Methods... // 13.1 The Monte Carlo Method of Computing Integrals ... // 13.1.1 Introduction... // 13.1.2 Estimating a probability... // 13.1.3 Estimating an expectation... // 13.2 Learning about the Sampling Distribution of a Statistic .. // 13.2.1 Simulating the sampling distribution by the Monte // Carlo method... // 13.2.2 Constructing a percentile confidence interval... // 13.3 Comparing Estimators... // 13.3.1 A simulation experiment... // 13.3.2 Estimating bias... // 13.3.3 Estimating mean distance from the target... // 13.4 Assessing Probability of Coverage ... // 13.4.1 A Monte Carlo experiment to compute a coverage // probability... // 13.5 Markov Chain Monte Carlo... // 13.5.1 Markov Chains... // 13.5.2 Metropolis-Hastings algorithm... // 13.5.3 Random walk Metropolis-Hastings algorithm ... // 13.5.4 Gibbs sampling... // 13.6 Further Reading... // Exercises ... // 307 // 307 // 307 // 308 // 309 // 311 // 312 312 // 314 // 315 // 315 // 316 318 // 318 // 321 // 321 // 324 // 328 // 331 // 333 // 333 // A Vectors, Matrices,

and Lists...337 // A.l Vectors... 337 // A.1.1 Creating a vector...337 // A. 1.2 Sequences... // A. 1.3 Extracting and replacing elements of vectors...338 // A.2 The sort and order functions...339 // A.3 Matrices ... 349 // A.3.1 Creating a matrix...34O // A.3.2 Arithmetic on matrices...342 // A.4 Lists... // A.5 Sampling from a data frame...347 // References // 349 // Index // 353