isiHistFit {STAR} | R Documentation |
Fits a duration model to isis from a spike train. Confidence intervals are also drawn.
isiHistFit(spikeTrain, model, nbins = 10, CI = 0.95, ...)
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. |
CI |
the confidence coefficient. |
... |
additional arguments passed to hist , see
hist . |
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.
Nothing returned, isiHistFit
is used for its side effect, a
plot is generated on the current graphic device.
Christophe Pouzat christophe.pouzat@gmail.com
## 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)