dcs {DCSmooth} | R Documentation |
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.
dcs(Y, dcs_options = set.options(), h = "auto", parallel = FALSE, ...)
Y |
A numeric matrix that contains the observations of the random field or functional time-series. |
dcs_options |
An object of class |
h |
Bandwidth for smoothing the observations in |
parallel |
A logical value indicating if parallel computing should be
used for faster computation. Default value is |
... |
Additional arguments passed to |
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). |
See the vignette for a more detailed description of the function.
SchÃ¤fer, B. and Feng, Y. (2021). Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series. Working Papers CIE 143, Paderborn University.
# See vignette("DCSmooth") for examples and explanation
y <- y.norm1 + matrix(rnorm(101^2), nrow = 101, ncol = 101)
dcs(y)