Description Usage Arguments Value Author(s) References See Also Examples
Given two time series, a set of combination coefficients, a function to combine them, this function makes the combination, tests the combination for stationarity, and returns the pvalue. Effectively, returns "how stationary" the combination is.
1 2 3 
par 
The coefficients used to make the combination via the

prodcomb.fn 
The function which computes the combination given the two time series and the combination parameters. 
tsx 
One of the time series. 
tsy 
The other time series. 
filter.number 
Wavelet smoothness to be used in the time series combination. 
family 
Wavelet family to be used in the time series combination. 
verbose 
Supplied directly to the call to 
tos 
The function the computes a test of stationarity 
Bsims 
Number of bootstrap simulations the test uses (if it does) 
lapplyfn 
The function used to process lists. Can be the regular

A single number between zero and one indicating the pvalue from the hypothesis test of stationarity of the combination.
G. P. 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  #
# Generate two toy time series data sets
#
x1 < rnorm(32)
y1 < rnorm(32)
#
# Generate two toy sets of parameters (for combination)
#
tmp.a < c(1,1)
tmp.b < c(0.5, 0.5)
#
# Call the function and find out the degree of stationarity of this
# combination
#
## Not run: ans < getpvals(c(tmp.a, tmp.b), prodcomb.fn=prodcomb, tsx=x1, tsy=y1,
filter.number=1, family="DaubExPhase")
## End(Not run)
#
# What is the pvalue?
#
## Not run: ans
# [1] 0.53

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