Description Usage Arguments Details Value Author(s) See Also Examples
Fits a duration model to isis from a spike train. Confidence intervals are also drawn.
1  isiHistFit(spikeTrain, model, nbins = 10, CI = 0.95, ...)

spikeTrain 
a 
model 
a character vector whose elements are selected among:

nbins 
the number of bins to use. 
CI 
the confidence coefficient. 
... 
additional arguments passed to 
Assuming that the train is reasonably well described by a renewal process,
a model
distribution is fitted to the interspike 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## 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)

Loading required package: survival
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.828. For overview type 'help("mgcvpackage")'.
Loading required package: R2HTML
Loading required package: gss
Loading required package: codetools
A spike train with 195 events, starting at: 0.318 and ending at: 30.379 (s).
The mean ISI is: 0.155 and its SD is: 0.283 (s).
The mean log(ISI) is: 2.838 and its SD is: 1.291
The shortest interval is: 0.007
and the longest is: 1.99 (s).
invgauss lnorm llogis weibull gamma rexp
477.1587 448.4228 444.7260 387.0661 364.5763 348.6731
Warning messages:
1: In rep(yi[si > 0], each = ni[si > 0]) :
first element used of 'each' argument
2: In rep(yi[si > 0], each = si[si > 0]) :
first element used of 'each' argument
3: In rep(yi[si > 0], each = si[si > 0]) :
first element used of 'each' argument
4: In rep(yi[si > 0], each = si[si > 0]) :
first element used of 'each' argument
5: In rep(yi[si > 0], each = si[si > 0]) :
first element used of 'each' argument
6: In rep(yi[si > 0], each = si[si > 0]) :
first element used of 'each' argument
7: In rep(yi[si > 0], each = si[si > 0]) :
first element used of 'each' argument
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