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gsspsth: Smooth Peri Stimulus Time Histogram Related Functions and Methods: The Smoothing Spline Approach

Description

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.

Usage

gsspsth(repeatedTrain, binSize = 0.025, plot = FALSE, ...)
gsspsth0(repeatedTrain, binSize = 0.025, plot = FALSE, ...)
"print"(x, ...)
"print"(x, ...)
"summary"(object, ...)
"summary"(object, ...)
"plot"(x, stimTimeCourse = NULL, colStim = "grey80", colCI = NULL, xlab, ylab, main, xlim, ylim, lwd = 2, col = 1, ...)
"plot"(x, stimTimeCourse = NULL, colStim = "grey80", colCI = NULL, xlab, ylab, main, xlim, ylim, lwd = 2, col = 1, ...)
"simulate"(object, nsim = 1, seed = NULL, ...)
"simulate"(object, nsim = 1, seed = NULL, ...)

Arguments

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.

Value

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.ssanova 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 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.

Details

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).

References

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.

See Also

psth, plot.psth, gssanova, gssanova0, summary.gssanova, summary.gssanova0, reportHTML.repeatedTrain, simulate

Examples

Run this code
## Not run: 
# ## 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)
# 
# ## 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$mids,Y_75,col=2,lwd=2)
# 
# ## simulate data from the first fitted model
# s1 <- simulate(n1CitrGSSPSTH0)
# ## look at the result
# s1
# 
# ## Do the same thing with gsspsth
# n1CitrGSSPSTH <- gsspsth(e070528citronellal[[1]])
# plot(n1CitrGSSPSTH,stim=c(6.14,6.64),colCI=2)
# summary(n1CitrGSSPSTH)
# plot(n1CitrGSSPSTH$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)

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