Data Analysis and Graphics Using R An Example-Based Approach

Name: Data Analysis and Graphics Using R An Example-Based Approach
Price: 10.34 USD
Availability: InStock
ISBN: 9780521813365

ISBN-10: 0521813360

ISBN-13: 9780521813365

Edition: 2003

Authors: John Maindonald, John A. Braun, R. Gill, B. D. Ripley, S. Ross

List price: $94.99

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Description:

Modern statistical software systems provide sophisticated tools for researchers who need to manipulate and display their data. Using such systems requires training both in the software itself and in the statistical methods that it relies on. Concentrating on the freely available R system, this book demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from the authors experience, both as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach…

Book details

List price: $94.99
Copyright year: 2003
Publisher: Cambridge University Press
Publication date: 8/4/2003
Binding: Hardcover
Pages: 386
Size: 7.25" wide x 10.25" long x 1.25" tall
Weight: 1.870
Language: English

John Maindonald is Visiting Fellow at the Mathematical Sciences Institute at the Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health to population genetics, machine learning, economic history, and forensic linguistics.



Preface


A Chapter by Chapter Summary



A Brief Introduction to R



A Short R Session



R must be installed!



Using the console (or command line) window



Reading data from a file



Entry of data at the command line



Online help



Quitting R



The Uses of R



The R Language



R objects



Retaining objects between sessions



Vectors in R



Concatenation--joining vector objects



Subsets of vectors



Patterned data



Missing values



Factors



Data Frames



Variable names



Applying a function to the columns of a data frame



Data frames and matrices



Identification of rows that include missing values



R Packages



Data sets that accompany R packages



Looping



R Graphics



The function plot () and allied functions



Identification and location on the figure region



Plotting mathematical symbols



Row by column layouts of plots



Graphs--additional notes



Additional Points on the Use of R in This Book



Further Reading



Exercises



Styles of Data Analysis



Revealing Views of the Data



Views of a single sample



Patterns in grouped data



Patterns in bivariate data--the scatterplot



Multiple variables and times



Lattice (trellis style) graphics



What to look for in plots



Data Summary



Mean and median



Standard deviation and inter-quartile range



Correlation



Statistical Analysis Strategies



Helpful and unhelpful questions



Planning the formal analysis



Changes to the intended plan of analysis



Recap



Further Reading



Exercises



Statistical Models



Regularities



Mathematical models



Models that include a random component



Smooth and rough



The construction and use of models



Model formulae



Distributions: Models for the Random Component



Discrete distributions



Continuous distributions



The Uses of Random Numbers



Simulation



Sampling from populations



Model Assumptions



Random sampling assumptions--independence



Checks for normality



Checking other model assumptions



Are non-parametric methods the answer?



Why models matter--adding across contingency tables



Recap



Further Reading



Exercises



An Introduction to Formal Inference



Standard Errors



Population parameters and sample statistics



Assessing accuracy--the standard error



Standard errors for differences of means



The standard error of the median



Resampling to estimate standard errors: bootstrapping



Calculations Involving Standard Errors: the t-Distribution



Confidence Intervals and Hypothesis Tests



One- and two-sample intervals and tests for means



Confidence intervals and tests for proportions



Confidence intervals for the correlation



Contingency Tables



Rare and endangered plant species



Additional notes



One-Way Unstructured Comparisons



Displaying means for the one-way layout



Multiple comparisons



Data with a two-way structure



Presentation issues



Response Curves



Data with a Nested Variation Structure



Degrees of freedom considerations



General multi-way analysis of variance designs



Resampling Methods for Tests and Confidence Intervals



The one-sample permutation test



The two-sample permutation test



Bootstrap estimates of confidence intervals



Further Comments on Formal Inference



Confidence intervals versus hypothesis tests



If there is strong prior information, use it!



Recap



Further Reading



Exercises



Regression with a Single Predictor



Fitting a Line to Data



Lawn roller example



Calculating fitted values and residuals



Residual plots



The analysis of variance table



Outliers, Influence and Robust Regression



Standard Errors and Confidence Intervals



Confidence intervals and tests for the slope



SEs and confidence intervals for predicted values



Implications for design



Regression versus Qualitative ANOVA Comparisons



Assessing Predictive Accuracy



Training/test sets, and cross-validation



Cross-validation--an example



Bootstrapping



A Note on Power Transformations



Size and Shape Data



Allometric growth



There are two regression lines!



The Model Matrix in Regression



Recap



Methodological References



Exercises



Multiple Linear Regression



Basic Ideas: Book Weight and Brain Weight Examples



Omission of the intercept term



Diagnostic plots



Further investigation of influential points



Example: brain weight



Multiple Regression Assumptions and Diagnostics



Influential outliers and Cook's distance



Component plus residual plots



Further types of diagnostic plot



Robust and resistant methods



A Strategy for Fitting Multiple Regression Models



Preliminaries



Model fitting



An example--the Scottish hill race data



Measures for the Comparison of Regression Models



R[superscript 2] and adjusted R[superscript 2]



AIC and related statistics



How accurately does the equation predict?



An external assessment of predictive accuracy



Interpreting Regression Coefficients--the Labor Training Data



Problems with Many Explanatory Variables



Variable selection issues



Principal components summaries



Multicollinearity



A contrived example



The variance inflation factor (VIF)



Remedying multicollinearity



Multiple Regression Models--Additional Points



Confusion between explanatory and dependent variables



Missing explanatory variables



The use of transformations



Non-linear methods--an alternative to transformation?



Further Reading



Exercises



Exploiting the Linear Model Framework



Levels of a Factor--Using Indicator Variables



Example--sugar weight



Different choices for the model matrix when there are factors



Polynomial Regression



Issues in the choice of model



Fitting Multiple Lines



Methods for Passing Smooth Curves through Data



Scatterplot smoothing--regression splines



Other smoothing methods



Generalized additive models



Smoothing Terms in Multiple Linear Models



Further Reading



Exercises



Logistic Regression and Other Generalized Linear Models



Generalized Linear Models



Transformation of the expected value on the left



Noise terms need not be normal



Log odds in contingency tables



Logistic regression with a continuous explanatory variable



Logistic Multiple Regression



A plot of contributions of explanatory variables



Cross-validation estimates of predictive accuracy



Logistic Models for Categorical Data--an Example



Poisson and Quasi-Poisson Regression



Data on aberrant crypt foci



Moth habitat example



Residuals, and estimating the dispersion



Ordinal Regression Models



Exploratory analysis



Proportional odds logistic regression



Other Related Models



Loglinear models



Survival analysis



Transformations for Count Data



Further Reading



Exercises



Multi-level Models, Time Series and Repeated Measures



Introduction



Example--Survey Data, with Clustering



Alternative models



Instructive, though faulty, analyses



Predictive accuracy



A Multi-level Experimental Design



The ANOVA table



Expected values of mean squares



The sums of squares breakdown



The variance components



The mixed model analysis



Predictive accuracy



Different sources of variance--complication or focus of interest?



Within and between Subject Effects--an Example



Time Series--Some Basic Ideas



Preliminary graphical explorations



The autocorrelation function



Autoregressive (AR) models



Autoregressive moving average (ARMA) models--theory



Regression Modeling with Moving Average Errors--an Example



Repeated Measures in Time--Notes on the Methodology



The theory of repeated measures modeling



Correlation structure



Different approaches to repeated measures analysis



Further Notes on Multi-level Modeling



An historical perspective on multi-level models



Meta-analysis



Further Reading



Exercises



Tree-based Classification and Regression



The Uses of Tree-based Methods



Problems for which tree-based regression may be used



Tree-based regression versus parametric approaches



Summary of pluses and minuses



Detecting Email Spam--an Example



Choosing the number of splits



Terminology and Methodology



Choosing the split--regression trees



Within and between sums of squares



Choosing the split--classification trees



The mechanics of tree-based regression--a trivial example



Assessments of Predictive Accuracy



Cross-validation



The training/test set methodology



Predicting the future



A Strategy for Choosing the Optimal Tree



Cost-complexity pruning



Prediction error versus tree size



Detecting Email Spam--the Optimal Tree



The one-standard-deviation rule



Interpretation and Presentation of the rpart Output



Data for female heart attack patients



Printed Information on Each Split



Additional Notes



Further Reading



Exercises



Multivariate Data Exploration and Discrimination



Multivariate Exploratory Data Analysis



Scatterplot matrices



Principal components analysis



Discriminant Analysis



Example--plant architecture



Classical Fisherian discriminant analysis



Logistic discriminant analysis



An example with more than two groups



Principal Component Scores in Regression



Propensity Scores in Regression Comparisons--Labor Training Data



Further Reading



Exercises



The R System--Additional Topics



Graphs in R



Functions--Some Further Details



Common useful functions



User-written R functions



Functions for working with dates



Data input and output



Input



Data output



Factors--Additional Comments



Missing Values



Lists and Data Frames



Data frames as lists



Reshaping data frames; reshape ()



Joining data frames and vectors--cbind ()



Conversion of tables and arrays into data frames



Merging data frames--merge ()



The function sapply () and related functions



Splitting vectors and data frames into lists--split ()



Matrices and Arrays



Outer products



Arrays



Classes and Methods



Printing and summarizing model objects



Extracting information from model objects



Data-bases and Environments



Workspace management



Function environments, and lazy evaluation



Manipulation of Language Constructs



Further Reading



Exercises


Epilogue--Models



S-PLUS Differences


References


Index of R Symbols and Functions


Index of Terms


Index of Names