prodcomb {costat} | R Documentation |
This function takes the cfs
vector and splits it
into two halves. The first half contains the wavelet coefficients
for the alpha
linear combination function, and the second
half for the beta
one. Then the functions themselves are
generated by using the coeftofn
function. Then, the
coefficient functions are multiplied by the respective time
series (tsx
by alpha
and tsy
by beta
)
and the result returned.
prodcomb(cfs, tsx, tsy, filter.number = 1, family = c("DaubExPhase", "DaubLeAsymm"), all = FALSE)
cfs |
Wavelet coefficients of the two combination functions. The coefficients for alpha/beta combination functions are stored in the first/last half of the vector. |
tsx |
The x time series to combine |
tsy |
The y time series to combine |
filter.number |
The wavelet filter to use to obtain functions from coefficients |
family |
The wavelet family to do the same. |
all |
If TRUE then a list containing the combined series
in the component |
This function is called by findstysols
and makes
use of coeftofn
to turn coefficients into a
function used in the combination.
If all=TRUE
then a list with the following components:
lcts |
The combined series, α_t X_t + β_t Y_t |
alpha |
The α_t combination function. |
beta |
The β_t combination function. |
If all=FALSE
then only lcts
is returned.
Guy Nason
Cardinali, A. and Nason, Guy P. (2013) Costationarity of Locally Stationary Time Series Using costat. Journal of Statistical Software, 55, Issue 1.
Cardinali, A. and Nason, G.P. (2010) Costationarity of locally stationary time series. J. Time Series Econometrics, 2, Issue 2, Article 1.
# # Toy example # tmp.a <- c(1, -1) tmp.b <- c(0.5, 0.5) # # Generate toy time series # xxx <- rnorm(256) yyy <- rnorm(256) # # Combine xxx and yyy using the functions produced by inverse wavelet # transform of tmp.a and tmp.b # ## Not run: tmp <- prodcomb(c(tmp.a, tmp.b), tsx=xxx, tsy=yyy) # # E.g. plot combination # ## Not run: ts.plot(tmp) # # Potentially test its stationarity.... etc #