Data Analysis and Graphics Using R: An Example-based ApproachJoin the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data. |
Contents
Section 1 | 43 |
Section 2 | 78 |
Section 3 | 81 |
Section 4 | 93 |
Section 5 | 101 |
Section 6 | 139 |
Section 7 | 144 |
Section 8 | 163 |
Section 15 | 292 |
Section 16 | 294 |
Section 17 | 301 |
Section 18 | 315 |
Section 19 | 350 |
Section 20 | 360 |
Section 21 | 363 |
Section 22 | 365 |
Section 9 | 173 |
Section 10 | 198 |
Section 11 | 219 |
Section 12 | 227 |
Section 13 | 246 |
Section 14 | 286 |
Section 23 | 375 |
Section 24 | 402 |
Section 25 | 404 |
Section 26 | 408 |
Section 27 | 421 |
Other editions - View all
Data Analysis and Graphics Using R: An Example-Based Approach John Maindonald,W. John Braun Limited preview - 2010 |
Common terms and phrases
alternative analysis of variance argument autocorrelation block bodywt bootstrap boxplot brainwt calculations Chapter coef coefficients columns comparison confidence interval Cook's distance correlation cross-validation curve DAAG DAAG package data frame data set default degrees of freedom density deviance discriminant discussion distance Error t value Estimate Std example explanatory variables F-statistic factor females Figure fitted values formula function gives graph graphics groups Intercept lattice levels linear logarithmic logarithmic scale logistic logistic regression males mean square measures median methods model matrix multiple names normal distribution Note objects observations obtained outliers output p-value panel parameter points population predictive accuracy principal components propensity score proportion random effects regression model residuals rows rpart scale scatterplot scores shows slope specify spline split standard deviation standard error statistical structure Subsection sum of squares summary transformation treatment tree tree-based variation vector weight workspace xyplot