%tt% {STAR} | R Documentation |
Performs time transformation using a gssanova
fit. If the model
is correct, the result of the transformation should be a Poisson
process with rate 1.
gssObj %tt% dataFrame
gssObj |
a gssanova or a gssanova0 object. |
dataFrame |
a data.frame with variables corresponding to
the ones used in the gssanova call giving rise to gssObj . |
The binary operator applies
predict.ssanova
with the left side as the first
argument and the right side as the second argument. The right side
(dataFrame
) must therefore contain the variables included in
the formula
used in the call giving rise to gssObj
. The
result of the predict
method call is then transformed with an
inverse logistic function or with an exponential (depending on the
family
argument, "binomial"
or "poisson"
, used in
the previous gssanova
call). The cumulative sum is
computed, that is, the integrated conditional intensity, and its value
at the events times is returned as a CountingProcessSamplePath
object.
A CountingProcessSamplePath
object.
Christophe Pouzat christophe.pouzat@gmail.com
Gu C. (2002) Smoothing Spline ANOVA Models. Springer.
Brillinger, D. R. (1988) Maximum likelihood analysis of spike trains of interacting nerve cells. Biol. Cybern. 59: 189–200.
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.
Ogata, Yosihiko (1988) Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes. Journal of the American Statistical Association 83: 9-27.
gssanova
,
predict.ssanova
,
mkGLMdf
,
mkCPSP
,
summary.CountingProcessSamplePath
## Not run: ## load e060517spont data set data(e060517spont) ## make a data frame using a 2 ms bin width e060517spontDF <- mkGLMdf(e060517spont,0.002,0,60) ## Keep data relevant to neuron 3 e060517spontDFn3 <- e060517spontDF[e060517spontDF$neuron == "3",] ## Split data in an "early" and a "late" part e060517spontDFn3e <- e060517spontDFn3[e060517spontDFn3$time <= 30,] e060517spontDFn3l <- e060517spontDFn3[e060517spontDFn3$time > 30,] ## fit the late part with a nonparametric renewal model e060517spontDFn3lGF <- gssanova(event ~ lN.3, data=e060517spontDFn3l,family="binomial") ## transform the time of the early part e060517spont.n3e.tt <- e060517spontDFn3lGF %tt% e060517spontDFn3e ## Test the goodness of fit e060517spont.n3e.tt summary(e060517spont.n3e.tt) plot(summary(e060517spont.n3e.tt)) ## End(Not run)