lpaws: Local polynomial smoothing by AWS

In aws: Adaptive Weights Smoothing

Local polynomial smoothing by AWS

Description

The function allows for structural adaptive smoothing using a local polynomial (degree <=2) structural assumption. Response variables are assumed to be observed on a 1 or 2 dimensional regular grid.

Usage

lpaws(y, degree = 1, hmax = NULL, aws = TRUE, memory = FALSE, lkern = "Triangle",
      homogen = TRUE, earlystop = TRUE, aggkern = "Uniform", sigma2 = NULL,
      hw = NULL, ladjust = 1, u = NULL, graph = FALSE, demo = FALSE)

Arguments

y	Response, either a vector (1D) or matrix (2D). The corresponding design is assumed to be a regular grid in 1D or 2D, respectively.
degree	Polynomial degree of the local model
hmax	maximal bandwidth
aws	logical: if TRUE structural adaptation (AWS) is used.
memory	logical: if TRUE stagewise aggregation is used as an additional adaptation scheme.
lkern	character: location kernel, either "Triangle", "Plateau", "Quadratic", "Cubic" or "Gaussian". The default "Triangle" is equivalent to using an Epanechnikov kernel, "Quadratic" and "Cubic" refer to a Bi-weight and Tri-weight kernel, see Fan and Gijbels (1996). "Gaussian" is a truncated (compact support) Gaussian kernel. This is included for comparisons only and should be avoided due to its large computational costs.
homogen	logical: if TRUE the function tries to determine regions where weights can be fixed to 1. This may increase speed.
earlystop	logical: if TRUE the function tries to determine points where the homogeneous region is unlikely to change in further steps. This may increase speed.
aggkern	character: kernel used in stagewise aggregation, either "Triangle" or "Uniform"
sigma2	Error variance, the value is estimated if not provided.
hw	Regularisation bandwidth, used to prevent from unidentifiability of local estimates for small bandwidths.
ladjust	factor to increase the default value of lambda
u	a "true" value of the regression function, may be provided to report risks at each iteration. This can be used to test the propagation condition with u=0
graph	logical: If TRUE intermediate results are illustrated graphically. May significantly slow down the computations in 2D. Please avoid using the default X11() on systems build with cairo, use X11(type="Xlib") instead (faster by a factor of 30).
demo	logical: if TRUE wait after each iteration

Value

returns anobject of class aws with slots

y = "numeric"	y
dy = "numeric"	dim(y)
x = "numeric"	numeric(0)
ni = "integer"	integer(0)
mask = "logical"	logical(0)
theta = "numeric"	Estimates of regression function and derivatives, length: length(y)*(degree+1)
mae = "numeric"	Mean absolute error for each iteration step if u was specified, numeric(0) else
var = "numeric"	approx. variance of the estimates of the regression function. Please note that this does not reflect variability due to randomness of weights.
xmin = "numeric"	numeric(0)
xmax = "numeric"	numeric(0)
wghts = "numeric"	numeric(0), ratio of distances wghts[-1]/wghts[1]
degree = "integer"	degree
hmax = "numeric"	effective hmax
sigma2 = "numeric"	provided or estimated error variance
scorr = "numeric"	0
family = "character"	"Gaussian"
shape = "numeric"	numeric(0)
lkern = "integer"	integer code for lkern, 1="Plateau", 2="Triangle", 3="Quadratic", 4="Cubic", 5="Gaussian"
lambda = "numeric"	effective value of lambda
ladjust = "numeric"	effective value of ladjust
aws = "logical"	aws
memory = "logical"	memory
homogen = "logical"	homogen
earlystop = "logical"	eralustop
varmodel = "character"	"Constant"
vcoef = "numeric"	numeric(0)
call = "function"	the arguments of the call to lpaws

Note

If you specify graph=TRUE for 2D problems avoid using the default X11() on systems build with cairo, use X11(type="Xlib") instead (faster by a factor of 30).

Author(s)

Joerg Polzehl [e-mail]

References

J. Polzehl, K. Papafitsoros, K. Tabelow (2020). Patch-Wise Adaptive Weights Smoothing in R, Journal of Statistical Software, 95(6), 1-27. doi:10.18637/jss.v095.i06 .

J. Polzehl, V. Spokoiny, in V. Chen, C.; Haerdle, W. and Unwin, A. (ed.) Handbook of Data Visualization Structural adaptive smoothing by propagation-separation methods. Springer-Verlag, 2008, 471-492. DOI:10.1007/978-3-540-33037-0_19.

See Also

link{awsdata},aws, aws.irreg

Examples

library(aws)
# 1D local polynomial smoothing
## Not run: demo(lpaws_ex1)
# 2D local polynomial smoothing
## Not run: demo(lpaws_ex2)

aws documentation built on Oct. 1, 2024, 1:08 a.m.