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.ssanova
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)