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Plotting of pointwise and highest pointwise probabilities.

Source: R/plot.HPWmapGrid.R
plot.HPWmapGrid.Rd

Maps with pointwise (PW) probabilities and/or highest pointwise (HPW) probabilities of all differences of smooths at neighboring scales are plotted.

Usage

# S3 method for class 'HPWmapGrid'
plot(
  x,
  plotWhich = "Both",
  color = c("firebrick1", "gainsboro", "dodgerblue3"),
  turnOut = TRUE,
  title,
  aspRatio = 1,
  ...
)

Arguments

x

List containing the pointwise (PW) and highest pointwise (HPW) probabilities of all differences of smooths.

plotWhich

Which probabilities shall be plotted? HPW, PW or Both?

color

Vector of length 3 containing the colors to be used in the credibility maps. The first color represents the credibly negative pixels, the second color the pixels that are not credibly different from zero and the third color the credibly positive pixels.

turnOut

Logical. Should the output images be turned 90 degrees counter-clockwise?

title

Vector containing one string per plot. The required number of titles is equal to length(mrbOut$hpout). If no title is passed, defaults are used.

aspRatio

Adjust the aspect ratio of the plots. The default aspRatio = 1 produces square plots.

...

Further graphical parameters can be passed.

Value

Plots of pointwise and/or highest pointwise probabilities for all differences of smooths are created.

Details

The default colors of the maps have the following meaning:

  • Blue: Credibly positive pixels.

  • Red: Credibly negative pixels.

  • Grey: Pixels that are not credibly different from zero.

x corresponds to the hpout-part of the output of mrbsizeRgrid.

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)

# Posterior mean of the differences of smooths
plot(x = mrbOut$smMean, turnOut = TRUE)


# Credibility analysis using pointwise (PW) maps
plot(x = mrbOut$hpout, plotWhich = "PW", turnOut = TRUE)


# Credibility analysis using highest pointwise probability (HPW) maps
plot(x = mrbOut$hpout, plotWhich = "HPW", turnOut = TRUE)