plot.frt: Plots and Summarizes frt Objects.
In STAR: Spike Train Analysis with R

Description Usage Arguments Details Value Author(s) See Also Examples

plot.frt generates interactively (by default) 2 plots, the survivor function with confidence intervals and the Berman's test with confidence bands. summary.frt generates a concise summary of frt objects. It is mostly intended for use in batch processing situations where a decision to stop with the current model or go on with a more complicated one must be made automatically.

## S3 method for class 'frt'
plot(x, which = 1:2, main,
         caption = c("Log Survivor Function", "Berman's Test"),
         ask = TRUE, ...)
## S3 method for class 'frt'
summary(object, ...)

`x`	a `transformedTrain` object.
`object`	a `transformedTrain` object.
`which`	if a subset of the plots is required, specify a subset of the numbers `1:2`.
`main`	title to appear above the plots, if missing the corresponding element of `caption` will be used.
`caption`	Default caption to appear above the plots or, if `main` is given, bellow it
`ask`	logical; if `TRUE`, the user is asked before each plot, see `par(ask=.)`.
`...`	additional arguments passed to `plot`.

If the reference and test (transformed) spike trains used in the frt call which generated x (or object) are not correlated (and if the transformed test train is indeed homogeneous Poisson with rate 1), the elements of x (or object) should be iid realizations of an exponential with rate 1. Two test plots are generated by plot.frt in the same way as the corresponding ones (testing the same thing) of plot.transformedTrain.

The same correspondence holds between summary.frt and summary.transformedTrain.

summary.frt returns a vector with named elements stating if the Berman's test is passed with a 95% and a 99% confidence.

Christophe Pouzat christophe.pouzat@gmail.com

transformedTrain, frt, mkGLMdf

## 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)