isiHistFit: ISI Histogram With Fitted Model and CI

Description

Fits a duration model to isis from a spike train. Confidence intervals are also drawn.

Usage

isiHistFit(spikeTrain, model, nbins = 10, CI = 0.95, ...)

Arguments

spikeTrain

a spikeTrain object or a numeric vector that can be coerced to such an object.

model

a character vector whose elements are selected among: "invgauss", "lnorm", "gamma", "weibull", "llogis", "rexp".

nbins

the number of bins to use.

the confidence coefficient.

...

additional arguments passed to hist, see hist.

Value

Nothing returned, isiHistFit is used for its side effect, a plot is generated on the current graphic device.

Details

Assuming that the train is reasonably well described by a renewal process, a model distribution is fitted to the inter-spike intervals (isis) obtained from spikeTrain. The fitted distribution is then used to set the histogram breaks such that a uniform bin count would be expected if the fitted distribution was the true one. Confidence segments are also obtained from the binomial distribution. The histogram is build and the fitted density together with confidence intervals are drawn.

Examples

Run this code

## Not run: 
# ## load spontaneous data of 4 putative projection neurons
# ## simultaneously recorded from the cockroach (Periplaneta
# ## americana) antennal lobe
# data(CAL1S)
# ## convert data into spikeTrain objects
# CAL1S <- lapply(CAL1S,as.spikeTrain)
# ## look at the individual trains
# ## first the "raw" data
# CAL1S[["neuron 1"]]
# ## next some summary information
# summary(CAL1S[["neuron 1"]])
# ## next the renewal tests
# renewalTestPlot(CAL1S[["neuron 1"]])
# ## It does not look too bad so let fit simple models
# compModels(CAL1S[["neuron 1"]])
# ## the best one is the invgauss. Let's look at
# ## it in detail
# isiHistFit(CAL1S[["neuron 1"]],"invgauss",xlim=c(0,0.5))
# ## End(Not run)

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