Modeling psychometric functions in R.
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.
Yssaad-Fesselier
Rosa
R
INSERM, U371, Bron, France.
Knoblauch
Kenneth
K
D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D017711:Nonlinear Dynamics; D011594:Psychometrics / Q000706:statistics & numerical data*