Modeling psychometric functions in R. Export

@article{YssaadKnoblauch:PsychFuncR, abstract = {We demonstrate some procedures in the statistical computing environment R for obtaining maximum likelihood estimates of the parameters of a psychometric function by fitting a generalized nonlinear regression model to the data. A feature for fitting a linear model to the threshold (or other) parameters of several psychometric functions simultaneously provides a powerful tool for testing hypotheses about the data and, potentially, for reducing the number of parameters necessary to describe them. Finally, we illustrate procedures for treating one parameter as a random effect that would permit a simplified approach to modeling stimulus-independent variability due to factors such as lapses or interobserver differences. These tools will facilitate a more comprehensive and explicit approach to the modeling of psychometric data.}, author = {Yssaad-Fesselier, Rosa and Knoblauch, Kenneth}, citeulike-article-id = {5839241}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/16817511}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=16817511}, issn = {1554-351X}, journal = {Behavior research methods}, keywords = {psychometric\_function}, month = {February}, number = {1}, pages = {28--41}, posted-at = {2009-09-25 09:35:12}, priority = {2}, title = {Modeling psychometric functions in R.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16817511}, volume = {38}, year = {2006} }

TY - JOUR ID - YssaadKnoblauch:PsychFuncR L3 - citeulike-article-id:5839241 N2 - We demonstrate some procedures in the statistical computing environment R for obtaining maximum likelihood estimates of the parameters of a psychometric function by fitting a generalized nonlinear regression model to the data. A feature for fitting a linear model to the threshold (or other) parameters of several psychometric functions simultaneously provides a powerful tool for testing hypotheses about the data and, potentially, for reducing the number of parameters necessary to describe them. Finally, we illustrate procedures for treating one parameter as a random effect that would permit a simplified approach to modeling stimulus-independent variability due to factors such as lapses or interobserver differences. These tools will facilitate a more comprehensive and explicit approach to the modeling of psychometric data. IS - 1 JF - Behavior research methods SN - 1554-351X EP - 41 TI - Modeling psychometric functions in R. VL - 38 SP - 28 KW - psychometric_function AU - Yssaad-Fesselier, Rosa AU - Knoblauch, Kenneth PY - 2006/02// UR - http://view.ncbi.nlm.nih.gov/pubmed/16817511 ER -