getPointwiseCIs-LagEstimator | R Documentation |
Returns a list of two arrays lowerCIs
and upperCIs
that contain
the upper and lower limits for a level 1-alpha
confidence interval of
the copula spectral density kernel. Each array is of dimension [J,K1,K2]
,
where J=length(frequencies)
, K1=length(levels.1)
, and
K2=length(levels.2))
.
At position (j,k1,k2)
the real (imaginary) part of the returned values
are the bounds of the confidence interval for the the real (imaginary) part
of the quantile spectrum, which corresponds to
frequencies[j]
, levels.1[k1]
and levels.2[k2]
closest
to the Fourier frequencies, levels.1
and levels.2
available in object
; closest.pos
is used to determine
what closest to means.
## S4 method for signature 'LagEstimator'
getPointwiseCIs(
object,
frequencies = 2 * pi * (0:(length(object@Y) - 1))/length(object@Y),
levels.1 = getLevels(object, 1),
levels.2 = getLevels(object, 2),
alpha = 0.1,
type = c("naive.sd", "boot.sd", "boot.full")
)
object |
|
frequencies |
a vector of frequencies for which to get the result |
levels.1 |
the first vector of levels for which to get the result |
levels.2 |
the second vector of levels for which to get the result |
alpha |
the level of the confidence interval; must be from |
type |
a flag indicating which type of confidence interval should be returned; can only take one values at the moment. |
Currently, only one type
of confidence interval is
available:
"naive.sd"
: confidence intervals based on the asymptotic
normality of the lag-window estimator; standard deviations
are estimated using getSdNaive
.
Returns a named list of two arrays lowerCIS
and upperCIs
containing the lower and upper bounds for the confidence intervals.
lagEst <- lagEstimator(rnorm(2^10), levels.1=0.5)
CI.upper <- Re(getPointwiseCIs(lagEst)$upperCIs[,1,1])
CI.lower <- Re(getPointwiseCIs(lagEst)$lowerCIs[,1,1])
freq = 2*pi*(0:1023)/1024
plot(x = freq, y = rep(0.25/(2*pi),1024),
ylim=c(min(CI.lower), max(CI.upper)),
type="l", col="red") # true spectrum
lines(x = freq, y = CI.upper)
lines(x = freq, y = CI.lower)
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