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The R book /

By: Crawley, Michael J.
Publisher: Chichester, West Sussex, UK : Wiley, 2013Edition: 2nd edition.Description: xxiv, 1051 p. : ill. (some coloured) ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780470973929; 9780470973929:; 0470973927.Subject(s): R (Computer program language) | Mathematical statistics -- Data processingDDC classification: 519.5
Contents:
Preface -- Getting started -- Essentials of the R language -- Dara input -- Dataframes -- Graphics -- Tables -- Mathematics -- Classical tests -- Statistical modelling -- Regression -- Analysis of variance -- Ananlysis of covariance -- Generalized linear models -- Count data -- Count data in tables -- Proportin data -- Binary repsonse variables -- Generalized additive models -- Mixed-effects models -- Non-linear regression -- Meta-analysis -- Bayesian statistics -- Tree models -- Time series analysis -- Multivariate statistics -- Spatial statistics -- Survival analysis -- Simulation models -- Changing the look of graphics -- References and further reading -- Index.
Summary: The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Standard Loan Standard Loan ATU Sligo Yeats Library Main Lending Collection 519.5 CRA (Browse shelf(Opens below)) 1 Lost Checked out 01/10/2019 0064191
Total holds: 0

Includes bibliographical references and index.

Preface -- Getting started -- Essentials of the R language -- Dara input -- Dataframes -- Graphics -- Tables -- Mathematics -- Classical tests -- Statistical modelling -- Regression -- Analysis of variance -- Ananlysis of covariance -- Generalized linear models -- Count data -- Count data in tables -- Proportin data -- Binary repsonse variables -- Generalized additive models -- Mixed-effects models -- Non-linear regression -- Meta-analysis -- Bayesian statistics -- Tree models -- Time series analysis -- Multivariate statistics -- Spatial statistics -- Survival analysis -- Simulation models -- Changing the look of graphics -- References and further reading -- Index.

The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.

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