000 03955nam a22004815i 4500
001 978-1-4614-4475-6
003 DE-He213
005 20140220083250.0
007 cr nn 008mamaa
008 120831s2012 xxu| s |||| 0|eng d
020 _a9781461444756
_9978-1-4614-4475-6
024 7 _a10.1007/978-1-4614-4475-6
_2doi
050 4 _aQA276-280
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aKnoblauch, Kenneth.
_eauthor.
245 1 0 _aModeling Psychophysical Data in R
_h[electronic resource] /
_cby Kenneth Knoblauch, Laurence T. Maloney.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2012.
300 _aXV, 365 p. 103 illus., 4 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R! ;
_v32
505 0 _aA 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 _aMany 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 _aStatistics.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics, general.
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
700 1 _aMaloney, Laurence T.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461444749
830 0 _aUse R! ;
_v32
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-4475-6
912 _aZDB-2-SMA
999 _c101500
_d101500