Multiresolution analysis of random signals for spherical data.

mrbsizeRSphere is the interface of the scale space multiresolution method for spherical data. Here, the differences of smooths as well as the posterior credibility analysis are computed. The output can be analyzed with the plotting functions plot.smMeanSphere, plot.CImapSphere and plot.HPWmapSphere.

mrbsizeRsphere(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)?

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.

Details

In contrast to mrbsizeRgrid, mrbsizeRsphere does not conduct Bayesian signal reconstruction via sampling from a posterior distribution. Samples of the posterior distribution have to be provided instead.

For further information and examples, see mrbsizeRgrid and the vignette.

Examples

# Artificial spherical sample data
set.seed(987)
sampleData <- matrix(stats::rnorm(2000), nrow = 200)
sampleData[50:65, ] <- sampleData[50:65, ] + 5

# mrbsizeRsphere analysis
mrbOut <- mrbsizeRsphere(posteriorFile = sampleData, mm = 10, nn = 20,
                          lambdaSmoother = c(1, 1000), prob = 0.95)