Modeling Psychophysical Data in R (Record no. 101500)

000 -LEADER
fixed length control field 03955nam a22004815i 4500
001 - CONTROL NUMBER
control field 978-1-4614-4475-6
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220083250.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120831s2012 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461444756
-- 978-1-4614-4475-6
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-1-4614-4475-6
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA276-280
072 #7 - SUBJECT CATEGORY CODE
Subject category code UFM
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM077000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Knoblauch, Kenneth.
Relator term author.
245 10 - TITLE STATEMENT
Title Modeling Psychophysical Data in R
Medium [electronic resource] /
Statement of responsibility, etc by Kenneth Knoblauch, Laurence T. Maloney.
264 #1 -
-- New York, NY :
-- Springer New York :
-- Imprint: Springer,
-- 2012.
300 ## - PHYSICAL DESCRIPTION
Extent XV, 365 p. 103 illus., 4 illus. in color.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement Use R! ;
Volume number/sequential designation 32
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note A First Tour through R by Example -- Modeling in R -- Signal Detection Theory (SDT) -- The Psychometric Function: Introduction -- The Psychometric Function: Continuation -- Classification Images -- Maximum Likelihood Difference Scaling (MLDS) -- Maximum Likelihood Conjoint Measurement (MLCM) -- Mixed-Effect Models -- Some Basics of R -- Statistical Background -- References -- Index.
520 ## - SUMMARY, ETC.
Summary, etc Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data. R is an open-source  programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods. This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France.  Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics and Computing/Statistics Programs.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical Theory and Methods.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics, general.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Maloney, Laurence T.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9781461444749
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Use R! ;
Volume number/sequential designation 32
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4614-4475-6
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-- ZDB-2-SMA

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