BayesBD (version 1.2)

parnormobs: Simulate Gaussian intensity images

Description

The generated data \(Y ~ N(\mu, \sigma)\) is the image intensity, where the params \((\mu, \sigma)\) are determined by whether the location \(X\), given by polar coords \((r,\theta)\), is inside the boundary or not.

Usage

parnormobs(m, mu.in, mu.out, sd.in, sd.out, design, center, gamma.fun)

Arguments

m

\(m * m\) observations will be generated over the unit square centered at (0,0).

mu.in

The mean intensity for pixels inside the image boundary.

mu.out

The mean intensity for pixels outside the image boundary.

sd.in

The standard deviation of intensity for pixels inside the image boundary.

sd.out

The standard deviation of intensity for pixels outside the image boundary.

design

Taking values: 'D' for deterministic (equally-spaced grid) design, 'U' for completely uniformly random, or 'J' for jitteredly random design.

center

a two-dimensional vector of Euclidean coordinates (x,y) of the reference point.

gamma.fun

The function to generate boundaries, see ellipse or triangle2.

Examples

# NOT RUN {
set.seed(2015)
# use ellipse boundary
gamma.fun = ellipse(a = 0.35, b = 0.25)
obs = parnormobs(m = 100, mu.in = 1, sd.in = 1, mu.out = 0, sd.out = 1, design = 'J',
 center = c(0.5,0.5), gamma.fun)
plotBD(obs)
# }