Pointwise (PW) probabilities and highest pointwise (HPW) probabilities of all differences of smooths at neighboring scales are computed.
HPWmap(smoothVec, mm, nn, prob = 0.95)
| 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 |
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.
HPWmap is an internal function of mrbsizeRgrid and is usually
not used independently. The output can be analyzed with the plotting function
plot.HPWmapGrid.
# 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 #>