Cvarip2 | R Documentation |
Performs precisely the same role as varip2
except it is implemented internally using C code and hence
is much faster.
Cvarip2(i, p, ll, S, Pmat, PsiJL)
i |
Scale parameter of Haar wavelet analyzing periodogram. Scale 1 is the finest scale. |
p |
Location parameter of Haar wavelet analyzing periodogram |
ll |
Scale of the raw wavelet periodogram being analyzed. |
S |
Estimate of the spectrum, under the assumption of stationarity.
So, this is just a vector of (possibly) J scales (which is often
the usual spectral estimate averaged over time). Note: that the
main calling function, |
Pmat |
Matrix version of autocorrelation wavelet computed
using the |
PsiJL |
True length of the autocorrelation wavelets
in the |
The list returned from the .C
calling function.
The only object of real interest is the ans
component
which contains the variance.
Guy Nason.
Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12015")}
hwtos2
,
varip2
#
# See example from varip2
#
#
my.Pmat <- PsiJmat(-5, filter.number=1, family="DaubExPhase")
my.PsiJ <- PsiJ(-5, filter.number=1, family="DaubExPhase")
my.PsiJL <- sapply(my.PsiJ, "length")
Cvarip2(i=1, p=10, ll=2, S=c(1/2,1/4,1/8,1/16,1/32),
Pmat=my.Pmat, PsiJL=my.PsiJL)
#
# Gives answer 1.865244, which is the same as given in the example for varip2
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.