aws.irreg {aws}  R Documentation 
The function implements the propagation separation approach to nonparametric smoothing (formerly introduced as Adaptive weights smoothing) for varying coefficient Gaussian models on a 1D or 2D irregulat design. The function allows for a paramertic (polynomial) meanvariance dependence.
aws.irreg(y, x, hmax = NULL, aws=TRUE, memory=FALSE, varmodel = "Constant", lkern = "Triangle", aggkern = "Uniform", sigma2 = NULL, nbins = 100, hpre = NULL, henv = NULL, ladjust =1, varprop = 0.1, graph = FALSE)
y 
The observed response vector (length n) 
x 
Design matrix, dimension n x d, 
hmax 

aws 
logical: if TRUE structural adaptation (AWS) is used. 
memory 
logical: if TRUE stagewise aggregation is used as an additional adaptation scheme. 
varmodel 
determines the model that relates variance to mean. Either "Constant", "Linear" or "Quadratic". 
lkern 
character: location kernel, either "Triangle", "Plateau", "Quadratic", "Cubic" or "Gaussian" 
aggkern 
character: kernel used in stagewise aggregation, either "Triangle" or "Uniform" 
sigma2 

nbins 
numer of bins, can be NULL, a positive integer or a vector of positive integers (length d) 
hpre 
smoothing bandwidth for initial variance estimate 
henv 
radius of balls around each observed design point where estimates will be calculated 
ladjust 
factor to increase the default value of lambda 
varprop 
exclude the largest 100*varprop% squared residuals when estimating the error variance 
graph 
If 
Data are first binned (1D/2D), then aws is performed on all datapoints within distance <= henv of nonempty bins.
returns anobject of class aws
with slots
y = "numeric" 
y 
dy = "numeric" 
dim(y) 
x = "numeric" 
x 
ni = "integer" 
number of observations per bin 
mask = "logical" 
bins where parameters have been estimated 
theta = "numeric" 
Estimates of regression function, 
mae = "numeric" 
numeric(0) 
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" 
vector of minimal xvalues (bins) 
xmax = "numeric" 
vector of maximal xvalues (bins) 
wghts = "numeric" 
relative binwidths 
degree = "integer" 
0 
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" 
FALSE 
earlystop = "logical" 
FALSE 
varmodel = "character" 
varmodel 
vcoef = "numeric" 
estimated coefficients in variance model 
call = "function" 
the arguments of the call to 
Joerg Polzehl, polzehl@wiasberlin.de
J. Polzehl, V. Spokoiny, in V. Chen, C.; Haerdle, W. and Unwin, A. (ed.) Handbook of Data Visualization Structural adaptive smoothing by propagationseparation methods. SpringerVerlag, 2008, 471492. DOI:10.1007/9783540330370_19.
See also lpaws
, link{awsdata}
, lpaws
require(aws) # 1D local constant smoothing ## Not run: demo(irreg_ex1) # 2D local constant smoothing ## Not run: demo(irreg_ex2)