Computation of pointwise and highest pointwise probabilities.

Pointwise (PW) probabilities and highest pointwise (HPW) probabilities of all differences of smooths at neighboring scales are computed.

Usage

HPWmap(smoothVec, mm, nn, prob = 0.95)

Arguments

smoothVec: Differences of smooths at neighboring scales.
mm: Number of rows of the original input image.
nn: Number of columns of the original input image.
prob: Credibility level for the posterior credibility analysis

Value

List with two arrays:

pw: Pointwise probabilities (VmapPW) including the dimensions of the original input image, mm and nn.
hpw: Highest pointwise probabilities (VmapHPW) including the dimensions of the original input image, mm and nn.

Details

HPWmap is an internal function of mrbsizeRgrid and is usually not used independently. The output can be analyzed with the plotting function plot.HPWmapGrid.

Examples

# Artificial sample data: 10 observations (5-by-2 object), 10 samples
set.seed(987)
sampleData <- matrix(stats::rnorm(100), nrow = 10)
sampleData[4:6, ] <- sampleData[4:6, ] + 5

# Calculation of the simultaneous credible intervals
HPWmap(smoothVec = sampleData, mm = 5, nn = 2, prob = 0.95)
#> $pw
#>      [,1] [,2]
#> [1,]  0.5  1.0
#> [2,]  0.5  0.5
#> [3,]  0.5  0.5
#> [4,]  1.0  0.5
#> [5,]  1.0  0.5
#> 
#> $hpw
#>      [,1] [,2]
#> [1,]  0.5  1.0
#> [2,]  0.5  0.5
#> [3,]  0.5  0.5
#> [4,]  1.0  0.5
#> [5,]  1.0  0.5
#>