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Linear mixed-effects models using R : (Record no. 418805)

000 -LEADER
fixed length control field 04000cam a22002657a 4500
001 - CONTROL NUMBER
control field 17344489
003 - CONTROL NUMBER IDENTIFIER
control field ISI Library, Kolkata
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20150115123311.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120613s2013 nyua b 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461438991 (alk. paper)
040 ## - CATALOGING SOURCE
Original cataloging agency ISI Library
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number 23
Item number G151
Classification number 000SA.062
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Gałecki, Andrzej.
245 10 - TITLE STATEMENT
Title Linear mixed-effects models using R :
Remainder of title a step-by-step approach /
Statement of responsibility, etc Andrzej Gałecki and Tomasz Burzykowski.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc New York :
Name of publisher, distributor, etc Springer,
Date of publication, distribution, etc c2013.
300 ## - PHYSICAL DESCRIPTION
Extent xxxii, 542 p. :
Other physical details ill. ;
Dimensions 24 cm.
490 ## - SERIES STATEMENT
Series statement Springer texts in statistics
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and indexes.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Part I Introduction -- <br/>1. Introduction -- <br/>2. Case Studies -- <br/>3. Data Exploration -- <br/><br/>Part II Linear Models for Independent Observations<br/>4. Linear Models with Homogeneous Variance --<br/>5. Fitting Linear Models with Homogeneous Variance: The lm() and gls() Functions -- <br/>6. ARMD Trial: Linear Model with Homogeneous Variance -- <br/>7. Linear Models with Heterogeneous Variance -- <br/>8. Fitting Linear Models with Heterogeneous Variance: The gls() Function -- <br/>9. ARMD Trial: Linear Model with Heterogeneous Variance -- <br/><br/>Part III Linear Fixed-effects Models for Correlated Data -- <br/>10. Linear Model with Fixed Effects and Correlated Errors -- <br/>11. Fitting Linear Models with Fixed Effects and Correlated Errors: The gls() Function -- <br/>12. ARMD Trial: Modeling Correlated Errors for Visual Acuity -- <br/><br/>Part VI Linear Mixed-effects Models -- <br/>13. Linear Mixed-Effects Model -- <br/>14. Fitting Linear Mixed-Effects Models: The lme()Function -- 15. Fitting Linear Mixed-Effects Models: The lmer() Function -- 16. ARMD Trial: Modeling Visual Acuity -- <br/>17. PRT Trial: Modeling Muscle Fiber Specific-Force -- <br/>18. SII Project: Modeling Gains in Mathematics Achievement-Scores -- <br/>19. FCAT Study: Modeling Attainment-Target Scores -- <br/>20. Extensions of the RTools for Linear Mixed-Effects Models--<br/><br/>Acronyms--<br/>References--<br/>Function Index--<br/>Subject Index.
520 ## - SUMMARY, ETC.
Summary, etc Preface Methods of Statistical Model Estimation has been written to develop a particular pragmatic viewpoint of statistical modelling. Our goal has been to try to demonstrate the unity that underpins statistical parameter estimation for a wide range of models. We have sought to represent the techniques and tenets of statistical modelling using executable computer code. Our choice does not preclude the use of explanatory text, equations, or occasional pseudo-code. However, we have written computer code that is motivated by pedagogic considerations first and foremost. An example is in the development of a single function to compute deviance residuals in Chapter 4. We defer the details to Section 4.7, but mention here that deviance residuals are an important model diagnostic tool for GLMs. Each distribution in the exponential family has its own deviance residual, defined by the likelihood. Many statistical books will present tables of equations for computing each of these residuals. Rather than develop a unique function for each distribution, we prefer to present a single function that calls the likelihood appropriately itself. This single function replaces five or six, and in so doing, demonstrates the unity that underpins GLM. Of course, the code is less efficient and less stable than a direct representation of the equations would be, but our goal is clarity rather than speed or stability. This book also provides guidelines to enable statisticians and researchers from across disciplines to more easily program their own statistical models using R. R, more than any other statistical application, is driven by the contributions of researchers who have developed scripts, functions, and complete packages for the use of others in the general research community--
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Linear models (Statistics).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element R (Computer program language).
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Burzykowski, Tomasz.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Books
Holdings
Lost status Not for loan Permanent Location Current Location Date acquired Cost, normal purchase price Full call number Accession Number Koha item type Original Price
    ISI Library, Kolkata ISI Library, Kolkata 2014-12-02 31383.10 000SA.062 G151 C26285 Books USD 50.59
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in


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