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.
1 2 3 4 5 6 
x 
a 
object 
a 
which 
if a subset of the plots is required, specify a subset of
the numbers 
main 
title to appear above the plots, if missing the
corresponding element of 
caption 
Default caption to appear above the plots or, if

ask 
logical; if 
... 
additional arguments passed to 
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  ## 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)

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