# General Random Guessing model

### Description

General Random Guessing model

### Usage

1 |

### 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

`glc`

,
`gqc`

,
`extractAIC.grg`

### Examples

1 2 3 4 | ```
data(subjdemo_2d)
fit.grand <- grg(subjdemo_2d$response, fixed=FALSE)
fit.frand <- grg(subjdemo_2d$response, fixed=TRUE)
``` |