grg {grt} | R Documentation |
General Random Guessing model
Description
General Random Guessing model
Usage
grg(response, fixed = FALSE, k = 2)
Arguments
response |
A vector containing participant's classification responses. |
fixed |
logical. If |
k |
numeric. the penalty per parameter to be used in calculating AIC. Default to 2. |
Details
The function assumes that there are two categories (e.g, ‘A’ and ‘B’) to which each stimulus belongs.
Fixed Random Guessing model assumes that participant responded randomly without response bias; for each stimulus, probability of responding ‘A’ and ‘B’ is .5. There are no free parameters in this model (i.e., df = 0).
General Random Guessing model assumes that participants responded randomly but is biased toward one response. The model estimates the response bias (df = 1).
Value
object of class grg
, which is a list object containing:
par |
the fixed or estimated response bias |
logLik |
the log-likelihood of the model |
AIC |
Akaike's An Information Criterion for the fitted model |
References
Ashby, F. G., & Crossley, M. J. (2010). Interactions between declarative and procedural-learning categorization systems. Neurobiology of Learning and Memory, 94, 1-12.
See Also
Examples
data(subjdemo_2d)
fit.grand <- grg(subjdemo_2d$response, fixed=FALSE)
fit.frand <- grg(subjdemo_2d$response, fixed=TRUE)