STAR (version 0.3-7)

summary.transformedTrain: Summary of transformedTrain Objects

Description

Generates a concise summary of transformedTrain 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

"summary"(object, ...)

Arguments

object

a transformedTrain object.

...

additional arguments passed to varianceTime.

Value

A list with the following 3 components:

uniformOnTTime

A two named components vector of boolean.

BermanTest

A two named components vector of boolean.

VarTime

A named component vector with as many components as passed to varianceTime via the ... argument with p-values of a binomial distribution.

Details

summary.transformedTrain computes summary statistics corresponding to plot 1, 2 and 5 of plot.transformedTrain.

The first plot tests the uniformity of the spikes (transformed) times on the (transformed) observation window using a KS test. If the ecdf of the (transformed) times is within the 95% band then the first element of component uniformOnTTime of the returned list is set to TRUE. It is set to FALSE otherwise. The second component is relative to the 99% band.

The second plot tests the exponential distribution of the intervals between successive spikes transformed times. Again if the empirical curve stays within the 95, respectively 99%, confidence band, the first, respectively second, element of component BermanTest of the returned list is set to TRUE. It is set to FALSE otherwise.

The fifth plot tests that the variance is equal to the length of the (transformed) observation time for object, using point-wise CI. If n different observation times are defined over the whole observation window, we expect (1 - CI/100)*n points to be out with an approximate binomial distribution. For each CI defined (95 and 99%, by default), component VarTime of the returned list contains the probability of observing a number as large as or smaller than the one observed under the binomial null hypothesis.

References

Ogata, Yosihiko (1988) Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes. Journal of the American Statistical Association 83: 9-27.

Brown, E. N., Barbieri, R., Ventura, V., Kass, R. E. and Frank, L. M. (2002) The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation 14: 325-346.

See Also

transformedTrain, plot.transformedTrain, 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)