acf.fnc: Autocorrelation trellis graph

In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics

Description

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

Usage

acf.fnc(dat, group="Subject", time="Trial", x = "RT", plot=TRUE, ...)

Arguments

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 Subject
time	A sequential time measure such as Trial number in the experimental list
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 layout

Value

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.

Author(s)

R. H. Baayen

References

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

See Also

lags.fnc

Examples

## Not run: 
data(beginningReaders)
acf.fnc(beginningReaders, x="LogRT")   # autocorrelations even though nonword responses not included

## End(Not run)

languageR documentation built on May 2, 2019, 10:02 a.m.