Data analysis and graphics using R : an example-based approach / John Maindonald and W. John Braun.

Format:

Book

Author/Creator:

Maindonald, J. H. (John Hilary), 1937- author.

Braun, John, 1963- author.

Series:

Cambridge series on statistical and probabilistic mathematics ; 10.

Cambridge series on statistical and probabilistic mathematics ; 10

Language:

English

Subjects (All):

Statistics--Data processing.

Statistics.

Statistics--Graphic methods--Data processing.

R (Computer program language).

Physical Description:

1 online resource (xxvi, 525 pages) : digital, PDF file(s).

Edition:

Third edition.

Other Title:

Data Analysis & Graphics Using R

Place of Publication:

Cambridge : Cambridge University Press, 2010.

Language Note:

English

Summary:

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), 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 third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

Contents:

A brief introduction to R

Styles of data analysis

Statistical models

A review of inference concepts

Regression with a single predictor

Multiple linear regression

Exploiting the linear model framework

Generalized linear models and survival analysis

Time series models

Multi-level models, and repeated measures

Tree-based classification and regression

Multivariate data exploration and discrimination

Regression on principal component or discriminant scores

The R system: additional topics

Graphs in R.

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references and indexes.

ISBN:

1-107-20823-8

0-511-71286-3

1-139-19464-X

1-282-58583-5

9786612585838

0-511-71369-X

0-511-71495-5

0-511-71619-2

0-511-72300-8

OCLC:

648739924