vpaws {aws}R Documentation

vector valued version of function paws with homogeneous covariance structure

Description

The function implements a vector-valued version the propagation separation approach that uses patches instead of individuel voxels for comparisons in parameter space. Functionality is analog to function vaws. Using patches allows for an improved handling of locally smooth functions and in 2D and 3D for improved smoothness of discontinuities at the expense of increased computing time.

Usage

vpaws(y, kstar = 16, sigma2 = 1, mask = NULL, scorr = 0, spmin = 0.25,
      ladjust = 1, wghts = NULL, u = NULL, maxni = FALSE, patchsize = 1)
vpawscov(y, kstar = 16, invcov = NULL, mask = NULL, scorr = 0, spmin = 0.25,
      ladjust = 1, wghts = NULL, u = NULL, maxni = FALSE, patchsize = 1)

Arguments

y

y contains the observed response data. dim(y) determines the dimensionality and extend of the grid design. First component varies over components of the response vector.

kstar

maximal number of steps to employ. Determines maximal bandwidth.

sigma2

specifies a homogeneous error variance.

invcov

array of voxelwise inverse covariance matrixes, first index corresponds to upper diagonal inverse covariance matrix.

mask

logical mask. All computations are restrikted to design poins within the mask.

scorr

The vector scorr allows to specify a first order correlations of the noise for each coordinate direction, defaults to 0 (no correlation).

spmin

determines the form (size of the plateau) in the adaptation kernel. Not to be changed by the user.

ladjust

factor to increase the default value of lambda

wghts

wghts specifies the diagonal elements of a weight matrix to adjust for different distances between grid-points in different coordinate directions, i.e. allows to define a more appropriate metric in the design space.

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

maxni

If TRUE use max_{l<=k}(N_i^{(l)} instead of (N_i^{(k)} in the definition of the statistical penalty.

patchsize

positive integer defining the size of patches. Number of grid points within the patch is (2*patchsize+1)^d with d denoting the dimensionality of the design.

Details

see vaws. The procedure is supposed to produce superior results if the assumption of a local constant image is violated or if smooothness of discontinuities is desired.

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, length: length(y)

hseq = "numeric"

sequence of bandwidths employed

mae = "numeric"

Mean absolute error for each iteration step if u was specified, numeric(0) else

psnr = "numeric"

Peak signal-to-noise ratio 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.Currently also uses factor 1/ni instead of the correct sum(wij^2)/ni^2

xmin = "numeric"

numeric(0)

xmax = "numeric"

numeric(0)

wghts = "numeric"

numeric(0), ratio of distances wghts[-1]/wghts[1]

degree = "integer"

0

hmax = "numeric"

effective hmax

sigma2 = "numeric"

provided or estimated error variance

scorr = "numeric"

scorr

family = "character"

family

shape = "numeric"

shape

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"

FALSE

varmodel = "character"

"Constant"

vcoef = "numeric"

numeric(0)

call = "function"

the arguments of the call to aws

Note

use setCores='number of threads' to enable parallel execution.

Author(s)

Joerg Polzehl, polzehl@wias-berlin.de, http://www.wias-berlin.de/people/polzehl/

References

Joerg Polzehl, Vladimir Spokoiny, Adaptive Weights Smoothing with applications to image restoration, J. R. Stat. Soc. Ser. B Stat. Methodol. 62 , (2000) , pp. 335–354

Joerg Polzehl, Vladimir Spokoiny, Propagation-separation approach for local likelihood estimation, Probab. Theory Related Fields 135 (3), (2006) , pp. 335–362.

See Also

See also vaws, lpaws, vawscov,link{awsdata}

Examples

## Not run: 
setCores(2)
y <- array(rnorm(4*64^3),c(4,64,64,64))
yhat <- vpaws(y,kstar=20)

## End(Not run)
[Package aws version 2.2-1 Index]