Given an object of class lomb
, this function allows the
reconstruction of the input signal using (a) a frequency selection
of single or multiple frequency (ranges), and/or (b) the most
significant peaks in the periodogram.
filter.lomb(
l = stop("No Lomb-Data"),
newx = NULL,
threshold = 6,
filt = NULL,
phase = "nextnb"
)
lomb object
vector of new values at which the restored function is to be evaluated
statistical threshold in terms of a standard deaviation of the amplidudes. It determines which frequencies are used. Lower values give more frequencies.
vector or matix of frequencies (ranges) in which to select the frequencies
set the method to determine the phase at a given frequency
This function returns a list which contains the reconstruction according to the
lomb
-object and newx
for the given data x
and y
. The returned
object contains the following:
x,y
reconstructed signal
f,A,phi
used parameters from the lomb
-object
p
corresponding significance values
To properly reconstruct the signal out of the calculated
lomb
-object, three different methods are available, which are
controlled by the filt
-argument.
If filt=NULL
, the most significant values in the (dense) spectrum
are used.
If filt=c(f1, .., fn)
, the given frequencies are used. The corresponding
phase is approximated.
If class(filt)=="matrix"
, each row of the 2 x n matrix defines a
frequency range. With in each range the "significant" frequencies are selected for
reconstruction.
Prior to the reconstruction the filter.lomb
-function calculates the
most significant amplitudes and corresponding phases. As a measure to select
the "correct" frequencies, the threshold
argument can be adjusted.
The corresponding phases of the underlying sine/cosine-waves are estimated by
one of the four following methods.
phase=="nextnb"
... use the phase of the bin of nearest neighbour.
phase=="lin"
... linear interpolation between the two closest bins.
phase=="lockin"
... principle of lock-in amplification, also known as
quadrature-demodulation technique.
phase=="fit"
... non-linear least squares fit with stats::nls