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.
mrbsizeRgrid(posteriorFile, mm, nn, lambdaSmoother, prob = 0.95, smoothOut = FALSE)
| 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)? |
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.
mrbsizeRgrid conducts two steps of the scale space multiresolution analysis:
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.
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.
# 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)