mrbsizeRgrid: Multiresolution analysis of random signals.

In romanflury/mrbsizeR: Scale Space Multiresolution Analysis of Random Signals

Description

mrbsizeRgrid is the interface of the scale space multiresolution method for data on a regular grid. Here, the differences of smooths as well as the posterior credibility analysis are computed. The output can be analyzed with the plotting functions plot.smMeanGrid, plot.CImapGrid and plot.HPWmapGrid.

Usage

mrbsizeRgrid(posteriorFile, mm, nn, lambdaSmoother, prob = 0.95,
  smoothOut = FALSE)

Arguments

posteriorFile	Matrix with posterior samples as column vectors.
mm	Number of rows of the original object.
nn	Number of columns of the original object.
lambdaSmoother	Vector consisting of the smoothing levels to be used.
prob	Credibility level for the posterior credibility analysis.
smoothOut	Should the differences of smooths at neighboring scales be returned as output (FALSE by default)?

Details

mrbsizeRgrid conducts two steps of the scale space multiresolution analysis:

1. Extraction of scale-dependent features from the reconstructed signal. This is done by smoothing at different smoothing levels and taking the difference of smooths at neighboring scales.

2. Posterior credibility analysis of the differences of smooths created. Three different methods are applied: Pointwise probabilities (see HPWmap), highest pointwise probabilities (see HPWmap) and simultaneous credible intervals (see CImap).

The signal can be reconstructed using the build-in multivariate t-distribution sampling rmvtDCT. It is also possible to provide samples generated with other methods, see the parameter posteriorFile and the examples.

For further information and examples, see the vignette.

Value

A list containing the following sublists:

smMean Posterior mean of all differences of smooths created.

hpout Pointwise (PW) and highest pointwise (HPW) probabilities of all differences of smooths created.

ciout Simultaneous credible intervals (CI) of all differences of smooths created.

smoothSamples Samples of differences of smooths at neighboring scales, as column vectors.

Examples

# Artificial sample data
set.seed(987)
sampleData <- matrix(stats::rnorm(100), nrow = 10)
sampleData[4:6, 6:8] <- sampleData[4:6, 6:8] + 5

# Generate samples from multivariate t-distribution
tSamp <- rmvtDCT(object = sampleData, lambda = 0.2, sigma = 6, nu0 = 15,
                  ns = 1000)  
 
# mrbsizeRgrid analysis
mrbOut <- mrbsizeRgrid(posteriorFile = tSamp$sample, mm = 10, nn = 10, 
                       lambdaSmoother = c(1, 1000), prob = 0.95)

romanflury/mrbsizeR documentation built on Dec. 15, 2019, 9:30 p.m.