LCTSres(res, tsx, tsy, inc = 0, solno = 1:nrow(res$endpar), filter.number = 1, family = "DaubExPhase", plot.it = FALSE, spec.filter.number = 1, spec.family = "DaubExPhase", plotcoef = FALSE, sameplot = TRUE, norm = FALSE, plotstystat = FALSE, plotsolinfo = TRUE, onlyacfs = FALSE, acfdatatrans = I, xlab = "Time", ...)spec.filter.number is the filter number of
this wavelet) used to compute the EWS can be different to the
one used to compute the lineasameplot is TRUE. This is so as to
be able to compare the patterns in each function without regard
to their overall size.plotcoef=FALSE) this option
causes the function to plot statistics associated with the
stationary solution, $$Z_t$$. The acf and partial acf are always
plotted. The time series plot of $$Z_t$$ and its spectrum are
optionalplotsolinfo=FALSE) this
option plots the $$alpha_t$$ linear combination function,
the $$beta_t$$ one (ie both of them), the stationary linear combination
$$Z_t$$,
and an estimate of the EWS of $$Z_t$$ computed using the
<plotstystat=TRUElog) to transform the series
before taking and displaying the acf functions.plot arguments are FALSE then no plots are
produced and the stationary linear combination of the (last)
solution number is returned.findstysols takes two time series
and attempts to find time-varying linear combinations of the
two that are stationary. If one is found, we call it $$Z_t$$.
However, findstysols works by numerical optimization,
typically from random starts, and, generally, there is no unique
stationary solution. This function takes the results obtained by findstysols
in an object called res and then for a set of solutions
already identifed by the user, and supplied to this function
via solno, this function takes each identified solution
in turn and produces a set of plots.
Determining which solutions are interesting is another problem.
The COEFbothscale is a useful function which
can analyze all solution sets simultaneously and, usually, arrange
them into groups which are mutually similar. Then representative
members from each group can be further analyzed by
LCTSres.
Probably the most useful set of options is
plotcoef=FALSE and to issue a
par(mfrow=c(2,2)) command prior to running
LCTSres. This produces the plots, four to a page,
and enables interesting features to be compared from plot to plot.
The plotcoef=FALSE option causes four plots to be produced
(on the same page if mfrow is set as the previous paragraph
suggests). The first two are the (potentially) time-varying linear
combination functions, the next is the stationary linear
combination, $$Z_t$$, itself and the final plot is an estimate of
the $$Z_t$$'s evolutionary wavelet spectrum. The titles of the latter
two plots display the process variance of $$Z_t$$ (the global
unconditional variance, because $$Z_t$$ is assumed to be stationary)
and the p-value associated the the hypothesis test of stationarity
of $$Z_t$$. The spectral estimate show exhibit near constancy because
of the stationarity (as assessed by hypothesis test) of $$Z_t$$.
If plotstystat=TRUE then further plots are produced
of the results of various classical time series analyses of $$Z_t$$.
If onlyacfs=TRUE then only the acf and partial acf of $$Z_t$$
are plotted, otherwise $$Z_t$$ and its classical spectrum are also
plotted (remember, $$Z_t$$, has tested to be stationary and so these
classical methods are valid).
If more than one solution is to be plotted, then the scan()
function is employed to pause the plots between plots.
findstysols#
# The example follows on directly from the one in the help page in findstysols
#
LCTSres(tmp, tsx=x2, tsy=y2, solno=1)Run the code above in your browser using DataLab