000 03840nam a22003855i 4500
001 288311
003 MX-SnUAN
005 20160429154623.0
007 cr nn 008mamaa
008 150903s2012 xxu| o |||| 0|eng d
020 _a9781461444756
_99781461444756
024 7 _a10.1007/9781461444756
_2doi
035 _avtls000341322
039 9 _a201509030829
_bVLOAD
_c201405050227
_dVLOAD
_y201402061056
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aKnoblauch, Kenneth.
_eautor
_9317959
245 1 0 _aModeling Psychophysical Data in R /
_cby Kenneth Knoblauch, Laurence T. Maloney.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2012.
300 _axv, 365 páginas 103 ilustraciones, 4 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aUse R! ;
_v32
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aMaloney, Laurence T.
_eautor
_9317960
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781461444749
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-4475-6
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c288311
_d288311