| acf.spikeTrain {STAR} | R Documentation |
The function acf.spikeTrain computes (and by default plots) estimates of the
autocovariance or autocorrelation function of the inter-spike
intervals of a spike train.
acf.spikeTrain(spikeTrain, lag.max = NULL,
type = c("correlation", "covariance", "partial"),
plot = TRUE, na.action = na.fail,
demean = TRUE, xlab = "Lag (in isi #)",
ylab = "ISI acf",
main, ...)
spikeTrain |
a spikeTrain object or a vector which can be
coerced to such an object. |
lag.max |
maximum lag at which to calculate the acf. Default is 10*log10(N) where N is the number of ISIs. Will be automatically limited to one less than the number of ISIs in the spike train. |
type |
character string giving the type of acf to be computed.
Allowed values are
"correlation" (the default), "covariance" or
"partial". |
plot |
logical. If TRUE (the default) the acf is plotted. |
na.action |
function to be called to handle missing
values. na.pass can be used. |
demean |
logical. Should the covariances be about the sample means? |
xlab |
x axis label. |
ylab |
y axis label. |
main |
title for the plot. |
... |
further arguments to be passed to plot.acf. |
Just a wrapper for acf function. The first argument,
spikeTrain, is processed first to extract the inter-spike
intervals. acf.spikeTrain is mainly used to plot what Perkel et
al (1967) call the serial correlation coefficient (Eq. 8) or
serial covariance coefficient (Eq. 7), p 400.
An object of class "acf", which is a list with the following
elements:
lag |
A three dimensional array containing the lags at which the acf is estimated. |
acf |
An array with the same dimensions as lag containing
the estimated acf. |
type |
The type of correlation (same as the type
argument). |
n.used |
The number of observations in the time series. |
series |
The name of the series x. |
snames |
The series names for a multivariate time series. |
The lag k value returned by ccf(x,y) estimates the
correlation between x[t+k] and y[t].
The result is returned invisibly if plot is TRUE.
Christophe Pouzat christophe.pouzat@gmail.com
Perkel D. H., Gerstein, G. L. and Moore G. P. (1967) Neural Spike Trains and Stochastic Point Processes. I. The Single Spike Train. Biophys. J., 7: 391-418. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=4292791
acf,
varianceTime,
renewalTestPlot
## Simulate a log normal train train1 <- c(cumsum(rlnorm(301,log(0.01),0.25))) train1 <- as.spikeTrain(train1) ## Get its isi acf acf.spikeTrain(train1,lag.max=100)