Description Usage Arguments Details Value Author(s) References See Also Examples
Combines the general wavelet test genwwn.test
at the mediumcoarse scales and the Haar wavelet test at
fine scales.
1 2 3 4  hywavwn.test(x, away.from = "standard", lowlev = 0, plot.it = FALSE,
stopeveryscale = FALSE, filter.number = 10,
family = "DaubExPhase", mc.method = p.adjust.methods,
verbose = FALSE, n.cdf.grid = 1000, mac.spread = 10)

x 
The time series you wish to test (of dyadic length). 
away.from 
Number of fine scales to stay away from, see details below. If "standard" then this is automatically computed for sample sizes up to length of 1024. If you have a longer series then the test will still work but might not be quite as powerful (but probably not too bad either). 
lowlev 
The coarsest coefficient to evaluate. This should always be left at 0. 
plot.it 
If TRUE then a series of plots similar to the ones produced
in the 
stopeveryscale 
If TRUE then if 
filter.number 
The number of vanishing moments of the wavelet used to compute coefficients that are then evaluated to see whether they are zero. In principle, best compression for a sparse evaluation of the normalized spectrum should mean we use the smoothest wavelets with the highest number of vanishing moments which is ten. The other components of the function are optimized for ten vanishing moments. The function will still work for other numbers of vanishing moments but maybe with slightly reduced power. 
family 
Wavelet family to go with 
mc.method 
The type of multiple hypothesis correction, see

verbose 
If 
n.cdf.grid 
The CDF of the Macdonald distribution is evaluated
numerically. This argument controls the resolution of that
grid: it controls the number of grid points there are between

mac.spread 
Horizontal range for plotting of wavelet coefficients,
only used if 
The genwwn.test
performs pretty well, but does
not pick up departures from the null at the finest scale of
wavelet coefficients because it does not look at those scales
(because of the ‘away.from’ argument and the asymptotic normality
that genwwn.test
does not kick in at those finer
scales). So, this test augments the genwwn.test
with the finest scales results from hwwn.test
.
Those scales finer than away.from
use the Haar wavelet
and those coarser than away.from
use the general wavelet.
An object of class htest
with the following components.
p.val.collector 
All the of unadjusted pvalues 
p.val.adjust 
All of the adjusted pvalues 
p.value 
The overall pvalue of the test 
method 
A text string describing the test 
p.val.collector.hw 
The of unadjusted pvalues from the Haar wavelet levels 
p.val.collector.gw 
The of unadjusted pvalues from the general wavelet levels 
Delyan Savchev and Guy Nason
Nason, G.P. and Savchev, D. (2014) White noise testing using wavelets. Stat, 3, 351362. http://dx.doi.org/10.1002/sta4.69
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  #
# Test data
#
x < rnorm(64)
#
# Do the test
#
answer < hywavwn.test(x)
#
# The result in my case was:
#
#answer
#
# Hybrid Wavelet Test of White Noise
#
#data:
#pvalue = 0.02305

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