plot.frt {STAR} | R Documentation |
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)