aws (version 1.8-0)

awsdata: Extract information from an object of class aws

Description

Extract data and estimates from an object of class aws

Usage

awsdata(awsobj, what)

Arguments

awsobj

an object of class aws

what

can be "data" (extracts observed response), "theta" (estimated parameters), "est" (estimated regression function), "var" (approx. variance of estimated regression function), "sd" (approx. standard deviation of estimated regression function), "sigma2" (e

Value

• an vector or array containing the specified information.

Details

The returned object is formatted as an array if appropriate. The returned object may be NULL if the information is not available.

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.

Joerg Polzehl, Vladimir 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

See Also

link{awsdata},aws, aws.irreg

Examples

require(aws)
# 1D local constant smoothing
demo(aws_ex1)
demo(aws_ex2)
# 2D local constant smoothing
demo(aws_ex3)
# 1D local polynomial smoothing
demo(lpaws_ex1)
# 2D local polynomial smoothing
demo(lpaws_ex2)
# 1D irregular design
demo(irreg_ex1)
# 2D irregular design 
demo(irreg_ex2)