binhf (version 1.0-3)

hfdenoise: Simulation function

Description

Proportion estimation procedure for simulations.

Usage

hfdenoise(n = 256, proportion = P2, binsize = 1, thrule = "ebayesthresh",
    van = 8, fam = "DaubLeAsymm", pl = 3, prior = "laplace", vscale = "independent", 
plotstep = FALSE, truncate = FALSE, ...)

Arguments

n

Length of vector to be sampled.

proportion

The function name of the proportion to be sampled.

binsize

The binomial size corresponding to the mean function proportion.

thrule

Thresholding procedure to be used in the smoothing. Possible values are "sureshrink" and "ebayesthresh".

van

the vanishing moments of the decomposing wavelet basis.

fam

the wavelet family to be used for the decomposing transform.Possible values are "DaubLeAsymm" and "DaubExPhase".

pl

the primary resolution to be used in the wavelet transform.

prior

Prior to be used in ebayesthresh thresholding.

vscale

argument to ebayesthresh thresholding procedure (variance calculation: "independent" or "bylevel").

plotstep

Should all steps be plotted in estimation procedure?

truncate

Should the estimates be truncated to lie in [0,1]?

...

Any other optional arguments.

Value

x

regular grid on which the proportion function is evaluated.

truep

vector corresponding to x of proportion function values.

fhat

Binomial Haar-Fisz estimate.

fhata

Anscombe inverse sine estimate.

fhatf

Freeman-Tukey average inverse sine estimate.

fl1

lokern estimate using binhf.wd as a preprocessor.

fl2

lokern estimate using Anscombe as a preprocessor.

bbwd

wd object of binomial Haar-Fisz before thresholding.

awd

wd object of Anscombe before thresholding.

b

data from which estimates were computed (sampled from truep.

bb

data after being preprocessed with binomial Haar-Fisz.

thr

Thresholded wd object of bbwd.

tmp

Thresholded (binomial Haar-Fisz) data before postprocessing.

Details

This function creates a regularly-spaced vector on the unit interval of length length, and uses these values to create corresponding values using the proportion function. These values are then used as binomial probabilities to sample "observed" binomial random variables. The observation vector is then denoised using a wavelet transform defined by the arguments pl, van, fam with thresholding method thrule. This denoising is done for both Anscombe and the Haar-Fisz method for binomial random variables. The procedure is repeated times times, and the resulting proportion estimates averaged.

See Also

simsij

Examples

# NOT RUN {
sim<-hfdenoise()

plot(sim$x,sim$truep,type="l", xlab="",ylab="Binomial Proportion")

##^^ shows original proportion to estimate.

lines(sim$x,sim$fhat,col=2)
lines(sim$x,sim$fhata,col=3)

##^^shows the estimates of the proportion from the two transforms.

# }