STAR (version 0.3-7)

plot.frt: Plots and Summarizes frt Objects.

Description

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.

Usage

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

Arguments

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.

Value

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

Details

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.

See Also

transformedTrain, 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)