Determines and returns an array of dimension [J,K1,K2]
,
where J=length(frequencies)
, K1=length(levels.1)
, and
K2=length(levels.2))
.
At position (j,k1,k2)
the returned value is the standard deviation
estimated corresponding to frequencies[j]
, levels.1[k1]
and
levels.2[k2]
that are closest to the frequencies
, levels.1
and levels.2
available in object
; closest.pos
is
used to determine what closest to means.
# S4 method for LagEstimator
getSdNaive(
object,
frequencies = 2 * pi * (0:(length(object@Y) - 1))/length(object@Y),
levels.1 = getLevels(object, 1),
levels.2 = getLevels(object, 2)
)
Returns the estimate described above.
LagEstimator
of which to get the estimates for the
standard deviation.
a vector of frequencies for which to get the result
the first vector of levels for which to get the result
the second vector of levels for which to get the result
Requires that the LagEstimator
is available at all Fourier
frequencies from \((0,\pi]\). If this is not the case the missing
values are imputed by taking one that is available and has a frequency
that is closest to the missing Fourier frequency; closest.pos
is used
to determine which one this is.
Note the ``standard deviation'' estimated here is not the square root of the complex-valued variance. It's real part is the square root of the variance of the real part of the estimator and the imaginary part is the square root of the imaginary part of the variance of the estimator.