simsij: Simulation function

Description

Proportion estimation procedure for simulations.

Usage

simsij(nsims = 100, n = 256, proportion = P2, binsize = 1,
    thrule = "ebayesthresh", van = 8, fam = "DaubLeAsymm", pl = 3,
    prior = "laplace",
    vscale = "independent", plotstep = FALSE, a = NA,truncate = FALSE, ...)

Arguments

nsims

The number of times to repeat the function doall (on random datasets from proportion).

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".

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?

the a argument for EbayesThresh.

truncate

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

...

Any other optional arguments.

Value

regular grid on which the proportion function is evaluated.

truep

vector corresponding to x of proportion function values.

ans

matrix containing the errors from each of the nsims doall runs.

est

Array containing the nsims estimates produced by Anscombe and Haar-Fisz.

bin

Matrix of the raw binomial samples for each of the nsims runs.

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 van, fam, min.level with thresholding method meth. 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.

Examples

Run this code

# NOT RUN {
a<-simsij(nsims=100)

plot(a$est[1,,1])


##^^ shows 1st binomial Haar-Fisz estimate.
# }