Set the smoothing specifications for locally weighted regression for identifying the trend and the seasonality in an equidistant time series.
set_options(
order_poly = 1,
season = NA_real_,
kernel_fun = "epanechnikov",
bwidth = NA_real_,
boundary_method = "extend"
)The function returns an S4 object with the following elements (access via
@):
identical to the input argument with that name; please see the description of that input argument.
identical to the input argument with that name; please see the description of that input argument.
identical to the input argument with that name; please see the description of that input argument.
identical to the input argument with that name; please see the description of that input argument.
identical to the input argument with that name; please see the description of that input argument.
the order of the local polynomials used for estimating the
smooth nonparametric trend; the default is 1.
the frequency of observations per time unit, for example per
year; set to 12 for monthly data and to 4 for quarterly data
and so on; the default is NA_real_, which leads to an automated
frequency selection for time series objects in smoothing functions; if
the argument is set to NA_real_ and the observations used for smoothing
are not formatted as time series objects, the frequency 1 will be
used.
the weighting function to consider; supported are four
second-order kernel functions with compact support on \([-1, 1]\); enter
"uniform" for the uniform kernel, "epanechnikov" for the
Epanechnikov kernel, "bisquare" for the bisquare kernel or
"triweight" for the triweight kernel; the default is
"epanechnikov".
a numeric value that indicates the relative bandwidth to
consider in the smoothing process; the default is NA, which then
triggers a data-driven selection of an globally optimal bandwidth when
the output of this function is passed to a smoothing function.
a single character value; it indicates, what bandwidth
method to use at boundary points; for "extend", the default, the
smoothing window around boundary points will be extended towards the center of
the data; for "shorten", the window width will keep decreasing at
boundary points when approaching the very first and the very last observation.
Dominik Schulz (Research Assistant) (Department of Economics, Paderborn
University),
Author and Package Creator
An object of class "smoothing_options" is created that contains
all required information to conduct a locally weighted regression for
decomposing a seasonal time series. The information include the order
of the trend polynomials, the frequency of the observed series, the
second-order kernel function to use in the weighting process, the
(relative) bandwidth to employ, and the boundary method for the bandwidth.