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nimblewomble: An R package for Bayesian wombling with nimble

The goal of nimblewomble is to perform Bayesian Wombling (boundary analysis) using nimble.

For more details on point-referenced Wombling please refer to: a. Bayesian Wombling: Sudipto Banerjee and Alan E. Gelfandhttps://doi.org/10.1198/016214506000000041 b. Bayesian Modeling with Curvature Processes: Aritra Halder, Sudipto Banerjee and Dipak K. Dey https://doi.org/10.1080/01621459.2023.2177166

Installation

You can install the development version of nimblewomble like so:

devtools::install_github("arh926/nimblewomble")

Example

Rplot02

This is a basic example which shows you the workflow on a simulated data:

Fitting a Gaussian Process

require(nimble)
require(nimblewomble)

set.seed(1)

N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), tau)

mc_sp = gp_fit(coords = coords, y = y, kernel = "matern1")

zbeta = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern1")

Predicitve Inference for Rates of Change

xsplit = ysplit = seq(-10, 10, by = 1)[-c(1, 21)]
grid = as.matrix(expand.grid(xsplit, ysplit), ncol = 2)
colnames(grid) = c("x", "y")
gradients = sprates(grid = grid,
                    coords = coords,
                    model = zbeta,
                    kernel = "matern1")
                    require(ggplot2)
require(ggplot2)
require(cowplot)
require(MBA)
require(metR)

p1 = sp_ggplot(data_frame = data.frame(grid,
                                       z = gradients$estimate.sx[,"50%"],
                                       sig = gradients$estimate.sx$sig))
p1
Rplot

Wombling Measure: Predicitve inference on Line Integrals

curve = # Pick a curve from the surface that is interesting to you
wm = spwombling(coords = coords,
                curve = curve,
                model = zbeta,
                kernel = "matern1")

col.pts = sapply(wm$estimate.wm$sig, function(x){
  if(x == 1) return("green")
  else if(x == -1) return("cyan")
  else return(NA)
})

p2 = sp_ggplot(obs, legend.key.height = 0.7, legend.key.width = 0.4, text.size = 10)

p2 + geom_path(curve, mapping = aes(x, y), linewidth = 2) + 
  geom_path(curve, mapping = aes(x, y), colour = c(col.pts, NA), linewidth = 1, na.rm = TRUE)
Rplot01

Authors

Name Email
Aritra Halder (maintainer) aritra.halder@drexel.edu Asst. Professor, Dept. of Biostatistics, Drexel Univ.
Sudipto Banerjee sudipto@ucla.edu Professor & Past Chair, Dept. of Biostatistics, UCLA

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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