grg: General Random Guessing model

In grt: General Recognition Theory

Description

General Random Guessing model

Usage

grg(response, fixed = FALSE, k = 2)

Arguments

response	A vector containing participant's classification responses.
fixed	logical. If TRUE, Fixed Random Guessing model is fitted. If FALSE, General Random Guessing model is fitted. see ‘Details’
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

glc, gqc, extractAIC.grg

Examples

data(subjdemo_2d)
fit.grand <- grg(subjdemo_2d$response, fixed=FALSE)

fit.frand <- grg(subjdemo_2d$response, fixed=TRUE)

grt documentation built on May 2, 2019, 7:10 a.m.