| gsslockedTrain {STAR} | R Documentation |
Smooths a lockedTrain object using a smoothing spline
(gssanova or gssanova0) with the Poisson
family after binning the object.
gsslockedTrain(lockedTrain, bw = 0.001, ...) gsslockedTrain0(lockedTrain, bw = 0.001, ...) ## S3 method for class 'gsslockedTrain': print(x, ...) ## S3 method for class 'gsslockedTrain0': print(x, ...) ## S3 method for class 'gsslockedTrain': summary(object, ...) ## S3 method for class 'gsslockedTrain0': summary(object, ...) ## S3 method for class 'gsslockedTrain': plot(x, xlab, ylab, main, xlim, ylim, col, lwd, ...) ## S3 method for class 'gsslockedTrain0': plot(x, xlab, ylab, main, xlim, ylim, col, lwd, ...)
lockedTrain |
a lockedTrain object. |
bw |
the bin width (in s) used to generate the observations on which the gss fit will be performed. See details below. |
x |
an gsslockedTrain or a gsslockedTrain0 object. |
object |
an gsslockedTrain or a gsslockedTrain0 object. |
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. |
... |
in gsslockedTrain, respectively gsslockedTrain0, the
... are passed to the internally called gssanova, repectively
gssanova0. Not used in print.gsslockedTrain and
summary.gsslockedTrain and their counterparts for
gsslockedTrain0 objects. Passed to plot in
plot.gsslockedTrain and plot.gsslockedTrain0. |
gsslockedTrain calls internally gssanova while
gsslockedTrain0 calls gssanova0. See the respective
documentations and references therein for an explanation of the differences.
gsslockedTrain and gsslockedTrain0 essentially generate
a smooth version of the
histogram obtained by hist.lockedTrain. The Idea is to
build the histogram first with a "too" small bin width before fitting
a regression spline to it with a Poisson distribution of the observed
counts.
A list of class gsslockedTrain, respectively gsslockedTrain0, is returned by
gsslockedTrain, respectively gsslockedTrain0. These
lists have the following components:
gssFit |
the gss object generated by
gssanova or gssanova0. |
Time |
the vector of bin centers. |
nRef |
the number of spikes in the reference train. See
hist.lockedTrain. |
testFreq |
the mean frequency of the test neuron. See
hist.lockedTrain. |
bwV |
the vector of bin widths used. |
CCH |
a logical which is TRUE if a cross-intensity was
estimated and FALSE in the case of an auto-intensity. |
call |
the matched call. |
print.gsslockedTrain returns the result of print
applied to the gssanova object generated by gsslockedTrain
and stored in the the component gssFit of its argument. The
same goes for print.gsslockedTrain0.
summary.gsslockedTrain returns the result of summary.gssanova
applied to the gssanova object generated by gsspsth
and stored in the component gssFit of its argument. The
same goes for summary.gsslockedTrain0.
Christophe Pouzat christophe.pouzat@gmail.com
Gu C. (2002) Smoothing Spline ANOVA Models. Springer.
lockedTrain,
plot.lockedTrain,
gssanova,
gssanova0
## load e070528spont data set data(e070528spont) ## create a lockedTrain object with neuron 1 as reference ## and neuron 3 as test up to lags of +/- 250 ms lt1.3 <- lockedTrain(e070528spont[[1]],e070528spont[[3]],laglim=c(-1,1)*0.25) ## look at the cross raster plot lt1.3 ## build a histogram of it using a 10 ms bin width hist(lt1.3,bw=0.01) ## do it the smooth way slt1.3 <- gsslockedTrain(lt1.3) plot(slt1.3) ## do some check on the gss fit summary(slt1.3) ## do the same with gsslockedTrain0 slt1.3 <- gsslockedTrain0(lt1.3) plot(slt1.3) ## do some check on the gss fit summary(slt1.3)