The `"aws"`

class is
used for objects obtained by functions `aws`

, `lpaws`

, `aws.irreg`

and `aws.gaussian`

.

Objects are created by calls to functions `aws`

, `lpaws`

, `aws.irreg`

and `aws.gaussian`

.

`.Data`

:Object of class

`"list"`

, usually empty.`y`

:Object of class

`"array"`

containing the original (response) data`dy`

:Object of class

`"numeric"`

dimension attribute of`y`

`x`

:Object of class

`"numeric"`

if provided the design points`ni`

:Object of class

`"numeric"`

sum of weights used in final estimate`mask`

:Object of class

`"logical"`

mask of design points where computations are performed`theta`

:Object of class

`"array"`

containes the smoothed object and in case of function`lpaws`

its derivatives up to the specified degree. Dimension is`dim(theta)=c(dy,p)`

`mae`

:Object of class

`"numeric"`

Mean absolute error with respect to array in argument`u`

if provided.`var`

:Object of class

`"numeric"`

pointwise variance of`theta[...,1]`

`xmin`

:Object of class

`"numeric"`

min of`x`

in case of irregular design`xmax`

:Object of class

`"numeric"`

max of`x`

in case of irregular design`wghts`

:Object of class

`"numeric"`

weights used in location penalty for different coordinate directions`degree`

:Object of class

`"integer"`

degree of local polynomials used in function`lpaws`

`hmax`

:Object of class

`"numeric"`

maximal bandwidth`sigma2`

:Object of class

`"numeric"`

estimated error variance`scorr`

:Object of class

`"numeric"`

estimated spatial correlation`family`

:Object of class

`"character"`

distribution of`y`

, can be any of`c("Gaussian","Bernoulli","Poisson","Exponential", "Volatility","Variance")`

`shape`

:Object of class

`"numeric"`

possible shape parameter of distribution of`y`

`lkern`

:Object of class

`"integer"`

location kernel, can be any of`c("Triangle","Quadratic","Cubic","Plateau","Gaussian")`

, defauts to`"Triangle"`

`lambda`

:Object of class

`"numeric"`

scale parameter used in adaptation`ladjust`

:Object of class

`"numeric"`

factor to adjust scale parameter with respect to its predetermined default.`aws`

:Object of class

`"logical"`

Adaptation by Propagation-Separation`memory`

:Object of class

`"logical"`

Adaptation by Stagewise Aggregation`homogen`

:Object of class

`"logical"`

detect regions of homogeneity (used to speed up the calculations)`earlystop`

:Object of class

`"logical"`

further speedup in function`lpaws`

estimates are fixed if sum of weigths does not increase with iterations.`varmodel`

:Object of class

`"character"`

variance model used in function`aws.gaussian`

`vcoef`

:Object of class

`"numeric"`

estimates variance parameters in function`aws.gaussian`

`call`

:Object of class

`"call"`

that created the object.

- extract
`signature(x = "aws")`

: ...- risk
`signature(y = "aws")`

: ...- plot
Method for Function ‘plot’ in Package ‘aws’.

- show
Method for Function ‘show’ in Package ‘aws’.

Method for Function ‘print’ in Package ‘aws’.

- summary
Method for Function ‘summary’ in Package ‘aws’.

Joerg Polzehl, polzehl@wias-berlin.de

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.

`aws`

, `lpaws`

, `aws.irreg`

, `aws.gaussian`

1 | ```
showClass("aws")
``` |

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