This function creates a trellis plot with autocorrelation functions for by-subject sequential dependencies in response latencies.

1 |

`dat` |
A data frame with (minimally) a grouping factor, an index for successive trails/events, and a behavioral measure |

`group` |
A grouping factor such as |

`time` |
A sequential time measure such as |

`x` |
The dependent variable, usually a chronometric measure such as RT |

`plot` |
If true, a trellis graph is produced, otherwise a data frame with the data on which the trellis graph is based is returned |

`...` |
other optional arguments, such as |

If `plot=TRUE`

, a trellis graph, otherwise a data frame with as column
names

`Lag` |
Autocorrelation lag |

`Acf` |
Autocorrelation |

`Subject` |
The grouping factor, typically Subject |

`ci` |
The (approximate) 95% confidence interval. |

R. H. Baayen

R. H. Baayen (2001) *Word Frequency Distributions*, Dordrecht: Kluwer.

lags.fnc

1 2 3 4 5 | ```
## Not run:
data(beginningReaders)
acf.fnc(beginningReaders, x="LogRT") # autocorrelations even though nonword responses not included
## End(Not run)
``` |

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