gsspsth {STAR} | R Documentation |
Function gsspsth
and gsspsth0
compute a smooth psth, while method
print.gsspsth
and print.gsspsth0
print and
summary.gsspsth
or summary.gsspsth0
summarize the
gssanova
/ gssanova0
objects contained in the returned gsspsth
or
gsspsth0
objects,
plot.gsspsth
or plot.gsspsth0
plot them and
simulate.gsspsth
or simulate.gsspsth0
simulate data from
fitted objects.
gsspsth(repeatedTrain, binSize = 0.025, plot = FALSE, ...) gsspsth0(repeatedTrain, binSize = 0.025, plot = FALSE, ...) ## S3 method for class 'gsspsth': print(x, ...) ## S3 method for class 'gsspsth0': print(x, ...) ## S3 method for class 'gsspsth': summary(object, ...) ## S3 method for class 'gsspsth0': summary(object, ...) ## S3 method for class 'gsspsth': plot(x, stimTimeCourse = NULL, colStim = "grey80", colCI = NULL, xlab, ylab, main, xlim, ylim, lwd = 2, col = 1, ...) ## S3 method for class 'gsspsth0': plot(x, stimTimeCourse = NULL, colStim = "grey80", colCI = NULL, xlab, ylab, main, xlim, ylim, lwd = 2, col = 1, ...) ## S3 method for class 'gsspsth': simulate(object, nsim = 1, seed = NULL, ...) ## S3 method for class 'gsspsth0': simulate(object, nsim = 1, seed = NULL, ...)
repeatedTrain |
a repeatedTrain object or a list which can be
coerced to such an object. |
binSize |
the bin size (in s) used to generate the observations on which the gss fit will be performed. See details below. |
plot |
corresponding argument of hist . Should a
plot be generated or not? |
object |
a gsspsth or a gsspsth0 object. |
x |
a gsspsth or a gsspsth0 object. |
stimTimeCourse |
NULL (default) or a two elements vector
specifying the time boundaries (in s) of a stimulus presentation. |
colStim |
the background color used for the stimulus. |
colCI |
if not NULL (default) a confidence band is
plotted with the specified color; two dashed lines are plotted otherwise. |
xlim |
a numeric (default value supplied). See
plot . |
ylim |
a numeric (default value supplied). See plot . |
xlab |
a character (default value supplied). See plot . |
ylab |
a character (default value supplied). See plot . |
main |
a character (default value supplied). See plot . |
lwd |
line width used to plot the estimated density. See plot . |
col |
color used to plot the estimated density. See
plot . |
nsim |
number of repeatedTrain objects to simulate. Defaults to 1. |
seed |
see simulate . |
... |
in gsspsth , respectively gsspsth0 , the
... are passed to the internally called gssanova , repectively
gssanova0 . In
plot.gsspsth and plot.gsspsth0 they are passed to
plot which is
called internally. They are not used otherwise. |
gsspsth
calls internally gssanova
while
gsspsth0
calls gssanova0
. See the respective
documentations and references therein for an explanation of the differences.
For both gsspsth
and gsspsth0
, the raw data contained in
repeatedTrain
are
pre-processed with hist
using a bin size given by
argument binSize
. This binSize
should be small "enough". That is, the
rate of the aggregated train created by collapsing the spike times of
the different trials onto a single "pseudo" spike train, should not
change too much on the scale of binSize
(see Ventura et al
(2002) Sec. 4.2 p8 for more details). Argument nbasis
of
gssanova
called internally by gsspsth
is set
to the number of bins of the histogram resulting from the
preprocessing stage.
simulate.gsspsth
and simulate.gsspsth0
perform exact
simuations of inhomogenous Poisson processes whose (time dependent)
rate/intensity function is accessible through the componenent
lambdaFct
of objects of class gsspsth
and
gsspsth0
. The inhomogenous Poisson processes are simulated with
the thinning method (Devroye, 1986, pp 253-256).
When plot
is set to FALSE
in gsspsth
, repectively
gsspsth0
, a list of
class gsspsth
, respectively gsspsth0
, is returned and no plot
is generated. These list have the following components:
freq |
a vector containing the instantaneous firing rate in the middle of the "thin" bins used for preprocessing. |
ciUp |
a vector with the upper limit of a pointwise 95% confidence interval. Check predict.gss for details. |
ciLow |
a vector with the lower limit of a pointwise 95% confidence interval. |
breaks |
a vector with 2 elements the ealiest and the latest spike in repeatedTrain . |
mids |
a numeric vector with the mid points of the bins. |
counts |
a vector with the actual number of spikes in each bin. |
nbTrials |
the number of trials in repeatedTrain . |
lambdaFct |
a function of a single time argument returning the estimated intensity (or instantaneous rate) at its argument. |
LambdaFct |
a function of a single time argument returning the
integrale of estimated intensity (or instantaneous rate) at its
argument. That is, the integrated intensity. integrate
is used by this function. |
call |
the matched call. |
When plot
is set to TRUE
nothing is returned and a plot
is generated as a side effect. Of course the same occurs upon calling
plot.gsspsth
with a gsspsth
object argument or
plot.gsspsth0
with a gsspsth0
.
print.gsspsth
returns the result of print.ssanova
applied to the gssanova
object generated by gsspsth
and stored in the environment
of both lambdaFct
and LambdaFct
. The same goes for print.gsspsth0
.
summary.gsspsth
returns the result of summary.gssanova
applied to the gssanova
object generated by gsspsth
and stored in the environment
of both lambdaFct
and LambdaFct
. The same goes for summary.gsspsth0
.
simulate.gsspsth
and simulate.gsspsth0
return a
repeatedTrain
object if argument nsim
is set to one and
a list of such objects if it is greater than one.
Most of the components of the list returned by gsspsth
and
gsspsth0
are not of
direct interest for the user but they are used by, for instance,
reportHTML.repeatedTrain
.
Christophe Pouzat christophe.pouzat@gmail.com
Gu C. (2002) Smoothing Spline ANOVA Models. Springer.
Ventura, V., Carta, R., Kass, R. E., Gettner, S. N. and Olson, C. R. (2002) Statistical analysis of temporal evolution in single-neuron firing rates. Biostatistics 3: 1–20.
Kass, R. E., Ventura, V. and Cai, C. (2003) Statistical smoothing of neuronal data. Network: Computation in Neural Systems 14: 5–15.
Devroye Luc (1986) Non-Uniform Random Variate Generation. Springer. Book available in pdf format at: http://cg.scs.carleton.ca/~luc/rnbookindex.html.
psth
,
plot.psth
,
gssanova
,
gssanova0
,
summary.gssanova
,
summary.gssanova0
,
reportHTML.repeatedTrain
,
simulate
## Get the e070528citronellal data set into workspace data(e070528citronellal) ## Compute gsspsth without a plot for neuron 1 ## using a smmothing spline with gssanova0, and default bin size of 25 ms. n1CitrGSSPSTH0 <- gsspsth0(e070528citronellal[[1]]) ## plot the result plot(n1CitrGSSPSTH0,stim=c(6.14,6.64),colCI=2) ## get a summary of the gss fit summary(n1CitrGSSPSTH0) ## Now take a look at the observation on which the gss ## was actually performed plot(n1CitrGSSPSTH0$mids,n1CitrGSSPSTH0$counts,type="l") ## Add the estimated smooth psth after proper scaling theBS <- diff(n1CitrGSSPSTH0[["mids"]])[1] Y <- n1CitrGSSPSTH0$lambdaFct(n1CitrGSSPSTH0$mids)*theBS*n1CitrGSSPSTH0$nbTrials lines(n1CitrGSSPSTH0$mids,Y,col=4,lwd=2) ## Not run: ## check the (absence of) effect of the pre-binning by using a smaller ## and larger one, say 10 and 75 ms n1CitrGSSPSTH0_10 <- gsspsth0(e070528citronellal[[1]],binSize=0.01) n1CitrGSSPSTH0_75 <- gsspsth0(e070528citronellal[[1]],binSize=0.075) ## plot the "high resolution" smoothed-psth plot(n1CitrGSSPSTH0_10,colCI="grey50") ## add to it the estimate obtained with the "low resolution" one Y_75 <- n1CitrGSSPSTH0_75$lambdaFct(n1CitrGSSPSTH0_10$mids) lines(n1CitrGSSPSTH0_10$mids,Y_75,col=2,lwd=2) ## End(Not run) ## simulate data from the first fitted model s1 <- simulate(n1CitrGSSPSTH0) ## look at the result s1 ## Not run: ## Do the same thing with gsspsth n1CitrGSSPSTH <- gsspsth(e070528citronellal[[1]]) plot(n1CitrGSSPSTH,stim=c(6.14,6.64),colCI=2) summary(n1CitrGSSPSTH) plot(n1CitrGSSPSTH$mids,n1CitrGSSPSTH$counts,type="l") theBS <- diff(n1CitrGSSPSTH[["mids"]])[1] Y <- n1CitrGSSPSTH$lambdaFct(n1CitrGSSPSTH$mids)*theBS*n1CitrGSSPSTH$nbTrials lines(n1CitrGSSPSTH$mids,Y,col=4,lwd=2) ## check the (absence of) effect of the pre-binning by using a smaller ## and larger one, say 10 and 75 ms n1CitrGSSPSTH_10 <- gsspsth(e070528citronellal[[1]],binSize=0.01) n1CitrGSSPSTH_75 <- gsspsth(e070528citronellal[[1]],binSize=0.075) ## plot the "high resolution" smoothed-psth plot(n1CitrGSSPSTH_10,colCI="grey50") ## add to it the estimate obtained with the "low resolution" one Y_75 <- n1CitrGSSPSTH_75$lambdaFct(n1CitrGSSPSTH_10$mids) lines(n1CitrGSSPSTH_10$mids,Y_75,col=2,lwd=2) ## simulate data from the first fitted model s1 <- simulate(n1CitrGSSPSTH) ## look at the result s1 ## End(Not run)