Skip to Main content Skip to Navigation
Journal articles

Modeling psychometric functions in R.

Rosa Yssaad-Fesselier 1 Kenneth Knoblauch 1, * 
* Corresponding author
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.
Document type :
Journal articles
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download
Contributor : Kenneth Knoblauch Connect in order to contact the contributor
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



  • HAL Id : inserm-00131799, version 1
  • PRODINRA : 251912
  • PUBMED : 16817511



Rosa Yssaad-Fesselier, Kenneth Knoblauch. Modeling psychometric functions in R.. Behavior Research Methods, Psychonomic Society, Inc, 2006, 38 (1), pp.28-41. ⟨inserm-00131799⟩



Record views


Files downloads