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Journal articles
Modeling psychometric functions in R.
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.
Cited literature [21 references]
https://www.hal.inserm.fr/inserm-00131799
Contributor : Kenneth Knoblauch <>
Submitted on : Tuesday, February 20, 2007 - 2:13:23 PM
Last modification on : Friday, May 29, 2020 - 11:09:23 PM
Long-term archiving on: : Wednesday, April 7, 2010 - 12:13:41 AM
Contributor : Kenneth Knoblauch <>
Submitted on : Tuesday, February 20, 2007 - 2:13:23 PM
Last modification on : Friday, May 29, 2020 - 11:09:23 PM
Long-term archiving on: : Wednesday, April 7, 2010 - 12:13:41 AM