DCSmooth (version 1.1.2)

dcs: Nonparametric Double Conditional Smoothing for 2D Surfaces

Description

dcs provides a double conditional nonparametric smoothing of the expectation surface of a functional time series or a random field on a lattice. Bandwidth selection is done via an iterative plug-in method.

Usage

dcs(Y, dcs_options = set.options(), h = "auto", parallel = FALSE, ...)

Arguments

Y

A numeric matrix that contains the observations of the random field or functional time-series.

dcs_options

An object of class "dcs_options", specifying the parameters for the smoothing and bandwidth selection procedure.

h

Bandwidth for smoothing the observations in Y. Can be a two-valued numerical vector with bandwidths in row- and column-direction. If the value is "auto" (the default), bandwidth selection will be carried out by the iterative plug-in algorithm.

parallel

A logical value indicating if parallel computing should be used for faster computation. Default value is parallel = FALSE. Parallelization seems to be efficient at above 400,000 observations.

...

Additional arguments passed to dcs. Currently supported are numerical vectors X and/or T containing the exogenous covariates with respect to the rows and columns.

Value

dcs returns an object of class "dcs", including

Y	matrix of original observations.
X, T	vectors of covariates over rows (X) and columns (T).
M	resulting matrix of smoothed values.
R	matrix of residuals of estimation, \(Y - M\).
h	optimized or given bandwidths.
c_f	estimated variance coefficient.
var_est	estimated variance model. If the variance function is modeled by an SARMA/SFARIMA, var_est is an object of class "sarma"/ "sfarima".
dcs_options	an object of class cds_options containing the initial options of the dcs procedure.
iterations	number of iterations of the IPI-procedure.
time_used	time spend searching for optimal bandwidths (not overall runtime of the function).

Details

See the vignette for a more detailed description of the function.

References

Sch<U+00E4>fer, B. and Feng, Y. (2021). Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series. Working Papers CIE 143, Paderborn University.

See Also

set.options

Examples

# NOT RUN {
# See vignette("DCSmooth") for examples and explanation

y <- y.norm1 + matrix(rnorm(101^2), nrow = 101, ncol = 101)
dcs(y)

# }