# Invent test data set which IS white noise
#
x <- rnorm(128)
#
# Do the test
#
x.wntest <- hwwn.test(x)
#
# Print the results
#
#x.wntest
#
# Wavelet Test of White Noise
#
#data:
#p-value = 0.9606
#
# So p-value indicates that there is no evidence for rejection of
# H_0: white noise.
#
# Let's do an example using data that is not white noise. E.g. AR(1)
#
x.ar <- arima.sim(n=128, model=list(ar=0.8))
#
# Do the test
#
x.ar.wntest <- hwwn.test(x.ar)
#
# Print the results
#
print(x.ar.wntest)
#
# Wavelet Test of White Noise
#
#data:
#p-value < 2.2e-16
#
# p-value is very small. Extremely strong evidence
# to reject H_0: white noise
#
#
# Let's use one of the other tests: e.g. the general wavelet one
#
x.ar.genwwntest <- genwwn.test(x.ar)
#
# Print the results
#
print(x.ar.genwwntest)
#
#
# Wavelet Test of White Noise
#
# data:
# p-value = 1.181e-10
#
# Again, p-value is very small
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