STAR (version 0.3-7)

frt: Computes Forward Recurrence Times from Two transformedTrain Objects

Description

Computes the (transformed) time differences between spikes of a refTrain and the (next) ones of a testTrain. Both refTrain and testTrain should be transformedTrain objects.

Usage

frt(refTrain, testTrain)
refTrain %frt% testTrain

Arguments

refTrain

a transformedTrain object.

testTrain

a transformedTrain object.

Value

An object of class frt containing the elapsed times.

Details

When two spike trains have been time transformed using the same procedure, which does make one of the trains (the testTrain) the realization a homogeneous Poisson process with rate 1, the elapsed time between the spikes of the other train (refTrain) and the ones of testTrain should be exponentially distributed with rate 1. These elapsed times are returned by frt.

See Also

transformedTrain, plot.frt, summary.frt, mkGLMdf

Examples

## Not run: 
# ## Let us consider neuron 1 of the CAL2S data set
# data(CAL2S)
# CAL2S <- lapply(CAL2S,as.spikeTrain)
# CAL2S[["neuron 1"]]
# renewalTestPlot(CAL2S[["neuron 1"]])
# summary(CAL2S[["neuron 1"]])
# ## Make a data frame with a 4 ms time resolution
# cal2Sdf <- mkGLMdf(CAL2S,0.004,0,60)
# ## keep the part relative to neuron 1, 2 and 3 separately
# n1.cal2sDF <- cal2Sdf[cal2Sdf$neuron=="1",]
# n2.cal2sDF <- cal2Sdf[cal2Sdf$neuron=="2",]
# n3.cal2sDF <- cal2Sdf[cal2Sdf$neuron=="3",]
# ## remove unnecessary data
# rm(cal2Sdf)
# ## Extract the elapsed time since the second to last and
# ## third to last for neuron 1. Normalise the result. 
# n1.cal2sDF[c("rlN.1","rsN.1","rtN.1")] <- brt4df(n1.cal2sDF,"lN.1",2,c("rlN.1","rsN.1","rtN.1"))
# ## load mgcv library
# library(mgcv)
# ## fit a model with a tensorial product involving the last
# ## three spikes and using a cubic spline basis for the last two
# ## To gain time use a fixed df regression spline
# n1S.fitA <- gam(event ~ te(rlN.1,rsN.1,bs="cr",fx=TRUE) + rtN.1,data=n1.cal2sDF,family=binomial(link="logit"))
# ## transform time
# N1.Lambda <- transformedTrain(n1S.fitA)
# ## check out the resulting spike train using the fact
# ## that transformedTrain objects inherit from spikeTrain
# ## objects
# N1.Lambda
# ## Use more formal checks
# summary(N1.Lambda)
# plot(N1.Lambda,which=c(1,2,4,5),ask=FALSE)
# ## Transform spike trains of neuron 2 and 3
# N2.Lambda <- transformedTrain(n1S.fitA,n2.cal2sDF$event)
# N3.Lambda <- transformedTrain(n1S.fitA,n3.cal2sDF$event)
# ## Check interactions
# summary(N2.Lambda %frt% N1.Lambda)
# summary(N3.Lambda %frt% N1.Lambda)
# plot(N2.Lambda %frt% N1.Lambda,ask=FALSE)
# plot(N3.Lambda %frt% N1.Lambda,ask=FALSE)
# ## End(Not run)