awsdata {aws}

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" (error variance), "mae" (mean absolute error for each iteration step, if available), "ni" (number of observations per bin), "mask" (logical indicator for bins where the regression function is estimated). "bi" (array of sum of weights or NULL) "bi2" (array of sum of squared weights or NULL)

Details

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

Value

an vector or array containing the specified information.

Author(s)

Joerg Polzehl [e-mail]

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
## Not run: demo(aws_ex1)
## Not run: demo(aws_ex2)
# 2D local constant smoothing
## Not run: demo(aws_ex3)
# 1D local polynomial smoothing
## Not run: demo(lpaws_ex1)
# 2D local polynomial smoothing
## Not run: demo(lpaws_ex2)
# 1D irregular design
## Not run: demo(irreg_ex1)
# 2D irregular design 
## Not run: demo(irreg_ex2)