Package 'nimblewomble' reference manual

Title:	Bayesian Wombling using Nimble
Description:	Performs Bayesian Wombling using Nimble. For more details on wombling please see, <doi:10.1198/016214506000000041> and <doi:10.1080/01621459.2023.2177166>.
Authors:	Aritra Halder [aut, cre], Sudipto Banerjee [aut]
Maintainer:	Aritra Halder <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2026-05-13 06:32:55 UTC
Source:	https://github.com/arh926/nimblewomble

Posterior samples of rates of change (gradients and curvatures) for the Matern kernel with $\nu\to\infty$ producing the squared exponential kernel.

Description

For internal use only.

Usage

curvatures_gaussian(dists.1, dists.2, dists.3, z, phi, sigma2)
curvatures_gaussian(dists.1, dists.2, dists.3, z, phi, sigma2)

Arguments

dists.1

distance matrix generated from coordinates

dists.2

distance of grid from coordinates

dists.3

delta = coordinate - grid

z

posterior samples of $Z(s)$

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::sprates()
 CG = compileNimble(curvatures_gaussian)
 sprates = CG(dists.1 = distM,
              dists.2 = dist.2,
              dists.3 = dist.3,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::sprates()
 CG = compileNimble(curvatures_gaussian)
 sprates = CG(dists.1 = distM,
              dists.2 = dist.2,
              dists.3 = dist.3,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)

Posterior samples of rates of change (gradients and curvatures) for the Matern kernel with $\nu=5/2$

Description

For internal use only.

Usage

curvatures_matern2(dists.1, dists.2, dists.3, z, phi, sigma2)
curvatures_matern2(dists.1, dists.2, dists.3, z, phi, sigma2)

Arguments

dists.1

distance matrix generated from coordinates

dists.2

distance of grid from coordinates

dists.3

delta = coordinate - grid

z

posterior samples of $Z(s)$

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::sprates()
 CM2 = compileNimble(curvatures_matern2)
 sprates = CM2(dists.1 = distM,
              dists.2 = dist.2,
              dists.3 = dist.3,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::sprates()
 CM2 = compileNimble(curvatures_matern2)
 sprates = CM2(dists.1 = distM,
              dists.2 = dist.2,
              dists.3 = dist.3,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)

Incomplete Gamma Function

Description

For internal use only. Use integration as a limit of a sum to numerically compute the incomplete gamma integral

Usage

gamma_int(x, a, b)
gamma_int(x, a, b)

Arguments

x

gamma quantile

a

shape parameter

b

scale parameter

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage in nimblewomble::wombling_matern1(...) or,
# nimblewomble::wombling_matern2(...)
gamma_int(x = 1, a = 1, b = 1/sqrt(3))

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage in nimblewomble::wombling_matern1(...) or,
# nimblewomble::wombling_matern2(...)
gamma_int(x = 1, a = 1, b = 1/sqrt(3))

## End(Not run)

Cross-covariance terms for the posterior distribution of wombling measures for Matern $\nu=3/2$ .

Description

For internal use only. Performs one-dimensional quadrature using integral as a limit of a sum.

Usage

gamma1.mcov1(coords, t, u, s0, phi)
gamma1.mcov1(coords, t, u, s0, phi)

Arguments

coords

coordinates

t

value of t

u

vector of u

s0

starting point on curve $s_0$

phi

posterior sample of $\phi$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside nimblewomble::wombling_matern1(...)
gamma1.mcov1(coords = coords[1:ncoords, 1:2], t = tvec[j],
             u = umat[j, 1:2], s0 = curve[j, 1:2], phi = phi[i])

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside nimblewomble::wombling_matern1(...)
gamma1.mcov1(coords = coords[1:ncoords, 1:2], t = tvec[j],
             u = umat[j, 1:2], s0 = curve[j, 1:2], phi = phi[i])

## End(Not run)

Cross-covariance terms for the posterior distribution of wombling measures for Matern $\nu\to\infty$ , the squared exponential kernel.

Description

For internal use only. Performs one-dimensional quadrature using integral as a limit of a sum.

Usage

gamma1n2.gauss(coords, t, u, s0, phi)
gamma1n2.gauss(coords, t, u, s0, phi)

Arguments

coords

coordinates

t

value of t

u

vector of u

s0

starting point on curve $s_0$

phi

posterior sample of $\phi$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside nimblewomble::wombling_gaussian(...)
gamma1n2.gauss(coords = coords[1:ncoords, 1:2], t = tvec[j],
               u = umat[j, 1:2], s0 = curve[j, 1:2], phi = phi[i])

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside nimblewomble::wombling_gaussian(...)
gamma1n2.gauss(coords = coords[1:ncoords, 1:2], t = tvec[j],
               u = umat[j, 1:2], s0 = curve[j, 1:2], phi = phi[i])

## End(Not run)

Cross-covariance terms for the posterior distribution of wombling measures for Matern $\nu=5/2$ , the squared exponential kernel.

Description

For internal use only. Performs one-dimensional quadrature using integral as a limit of a sum.

Usage

gamma1n2.mcov2(coords, t, u, s0, phi)
gamma1n2.mcov2(coords, t, u, s0, phi)

Arguments

coords

coordinates

t

value of t

u

vector of u

s0

starting point on curve $s_0$

phi

posterior sample of $\phi$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside nimblewomble::wombling_matern2(...)
gamma1n2.mcov2(coords = coords[1:ncoords, 1:2], t = tvec[j],
               u = umat[j, 1:2], s0 = curve[j, 1:2], phi = phi[i])

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside nimblewomble::wombling_matern2(...)
gamma1n2.mcov2(coords = coords[1:ncoords, 1:2], t = tvec[j],
               u = umat[j, 1:2], s0 = curve[j, 1:2], phi = phi[i])

## End(Not run)

Squared Exponential Covariance kernel

Description

Computes the Matern covariance matrix with fractal parameter $\nu \to \infty$ . Has the option to compute $\Sigma_{d\times d} + \tau^2 I_d$ .

Usage

gaussian(dists, phi, sigma2, tau2)
gaussian(dists, phi, sigma2, tau2)

Arguments

dists

distance matrix

phi

spatial range

sigma2

spatial variance

tau2

nugget variance

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Used across multiple functions
# Example usage
gaussian(dists = dists[1:ncoords, 1:ncoords], phi = phi[i], sigma2 = 1, tau2 = 0)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Used across multiple functions
# Example usage
gaussian(dists = dists[1:ncoords, 1:ncoords], phi = phi[i], sigma2 = 1, tau2 = 0)

## End(Not run)

Fit a Gaussian process

Description

Fits a Gaussian process with the choice of three kernels. Uses 'nimble' to generate posterior samples.

Usage

gp_fit(
  coords = NULL,
  y = NULL,
  X = NULL,
  kernel = c("matern1", "matern2", "gaussian"),
  niter = NULL,
  nburn = NULL
)
gp_fit(
  coords = NULL,
  y = NULL,
  X = NULL,
  kernel = c("matern1", "matern2", "gaussian"),
  niter = NULL,
  nburn = NULL
)

Arguments

coords

spatial coordinats (supply as a matrix)

y

response

X

covariates (supply as a matrix without the intercept)

kernel

choice of kernel; must be one of "matern1", "matern2", "gaussian"

niter

number of iterations

nburn

burn-in

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
require(nimble)
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2)
colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)
# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
mc_sp$estimates

## End(Not run)
## Not run: 
require(nimble)
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2)
colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)
# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
mc_sp$estimates

## End(Not run)

Posterior samples of rates of change (gradients) for the Matern kernel with $\nu=3/2$

Description

For internal use only.

Usage

gradients_matern1(dists.1, dists.2, dists.3, z, phi, sigma2)
gradients_matern1(dists.1, dists.2, dists.3, z, phi, sigma2)

Arguments

dists.1

distance matrix generated from coordinates

dists.2

distance of grid from coordinates

dists.3

delta = coordinate - grid

z

posterior samples of $Z(s)$

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::sprates()
 GM1 = compileNimble(gradients_matern1)
 sprates = GM!(dists.1 = distM,
              dists.2 = dist.2,
              dists.3 = dist.3,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::sprates()
 GM1 = compileNimble(gradients_matern1)
 sprates = GM!(dists.1 = distM,
              dists.2 = dist.2,
              dists.3 = dist.3,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)

Matern Covariance kernel with $\nu = 3/2$

Description

Computes the Matern covariance matrix with fractal parameter $\nu = 3/2$ . Has the option to compute $\Sigma_{d\times d} + \tau^2 I_d$ .

Usage

materncov1(dists, phi, sigma2, tau2)
materncov1(dists, phi, sigma2, tau2)

Arguments

dists

distance matrix

phi

spatial range

sigma2

spatial variance

tau2

nugget variance

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Used across multiple functions
# Example usage
materncov1(dists = dists[1:ncoords, 1:ncoords], phi = phi[i], sigma2 = 1, tau2 = 0)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Used across multiple functions
# Example usage
materncov1(dists = dists[1:ncoords, 1:ncoords], phi = phi[i], sigma2 = 1, tau2 = 0)

## End(Not run)

Matern Covariance kernel with $\nu = 5/2$

Description

Computes the Matern covariance matrix with fractal parameter $\nu = 5/2$ . Has the option to compute $\Sigma_{d\times d} + \tau^2 I_d$ .

Usage

materncov2(dists, phi, sigma2, tau2)
materncov2(dists, phi, sigma2, tau2)

Arguments

dists

distance matrix

phi

spatial range

sigma2

spatial variance

tau2

nugget variance

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Used across multiple functions
# Example usage
materncov2(dists = dists[1:ncoords, 1:ncoords], phi = phi[i], sigma2 = 1, tau2 = 0)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Used across multiple functions
# Example usage
materncov2(dists = dists[1:ncoords, 1:ncoords], phi = phi[i], sigma2 = 1, tau2 = 0)

## End(Not run)

Computes the Cumulative Distribution Function for the standard Gaussian probability distribution

Description

For internal use only.

Usage

pnorm_nimble(x)
pnorm_nimble(x)

Arguments

x

standard Gaussian quantile

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::wombling_gaussian()
pnorm_nimble(sqrt(2) * phi[i] * tvec[j]) - 1)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::wombling_gaussian()
pnorm_nimble(sqrt(2) * phi[i] * tvec[j]) - 1)

## End(Not run)

Determines significance for posterior estimates

Description

For internal use only.

Usage

significance(data_frame = NULL)
significance(data_frame = NULL)

Arguments

data_frame

matrix consisting of median, lower and upper Confidence Interval

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
estimate.wm$sig = significance(estimate.wm)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
estimate.wm$sig = significance(estimate.wm)

## End(Not run)

Spatial Plot Function

Description

Spatial Plot Function

Usage

sp_ggplot(
  data_frame = NULL,
  sp = FALSE,
  shape = NULL,
  legend.key.height = 0.7,
  legend.key.width = 0.4,
  text.size = 10,
  point.size = 0.7,
  clr.pt = "black",
  palette = "Spectral",
  extend = TRUE,
  title = NULL,
  bound.box = NULL
)
sp_ggplot(
  data_frame = NULL,
  sp = FALSE,
  shape = NULL,
  legend.key.height = 0.7,
  legend.key.width = 0.4,
  text.size = 10,
  point.size = 0.7,
  clr.pt = "black",
  palette = "Spectral",
  extend = TRUE,
  title = NULL,
  bound.box = NULL
)

Arguments

data_frame

data frame consisting of coordinates and data

sp

logical parameter indicating whether to make a spatial plot

shape

if sp = TRUE shape file should be provided (should be an sf object)

legend.key.height

height of legend (defaults to .7)

legend.key.width

width of legend (defaults to .4)

text.size

size of legend text (defaults to 10)

point.size

size of points to be plotted (defaults to 0.7)

clr.pt

color of point to be plotted (defaults to black)

palette

(optional) color palette

extend

logical parameter indicating whether to extend the interpolation (defaults to TRUE)

title

title of the plot (defaults to NULL)

bound.box

bounding box for spatial maps (leave as NULL if not known)

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2)
colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

sp_ggplot(data_frame = data.frame(coords, z = y))
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2)
colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

sp_ggplot(data_frame = data.frame(coords, z = y))

Posterior samples for rates of change

Description

Posterior samples for rates of change

Usage

sprates(
  coords = NULL,
  grid = NULL,
  model = NULL,
  kernel = c("matern1", "matern2", "gaussian")
)
sprates(
  coords = NULL,
  grid = NULL,
  model = NULL,
  kernel = c("matern1", "matern2", "gaussian")
)

Arguments

coords

coordinates

grid

grid for sampling the rates of change

model

posterior samples of $Z(s)$ , $\phi$ , $\sigma^2$

kernel

choice of kernel; must be one of "matern1", "matern2", "gaussian"

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
require(nimble)
require(nimblewomble)
require(patchwork)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

# Create equally spaced grid of points
xsplit = ysplit = seq(-10, 10, by = 1)[-c(1, 21)]
grid = as.matrix(expand.grid(xsplit, ysplit), ncol = 2)
colnames(grid) = c("x", "y")

####################################
# Process for True Rates of Change #
####################################
# Gradient along x
true_sx = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                grid[,1]/sqrt(grid[,1]^2 + grid[,2]^2), 3)
# Gradient along y
true_sy = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                grid[,2]/sqrt(grid[,1]^2 + grid[,2]^2), 3)
# Curvature along x
true_sxx = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2))/
                  sqrt(grid[,1]^2 + grid[,2]^2) -
                 20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,1]^2/(grid[,1]^2 + grid[,2]^2)^(3/2) -
                 20 * sin(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,1]^2/(grid[,1]^2 + grid[,2]^2), 3)
# Mixed Curvature
true_sxy = round(-20 * (cos(sqrt(grid[,1]^2 + grid[,2]^2)) -
                 sin(sqrt(grid[,1]^2 + grid[,2]^2))) * grid[,1]
                  * grid[,2]/(grid[,1]^2 + grid[,2]^2), 3)
# Curvature along y
true_syy = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2))/
                  sqrt(grid[,1]^2 + grid[,2]^2) -
                 20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,2]^2/(grid[,1]^2 + grid[,2]^2)^(3/2) -
                 20 * sin(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,2]^2/(grid[,1]^2 + grid[,2]^2), 3)
# Create the plots
p1 = sp_ggplot(data_frame = data.frame(coords, z = y))
p2 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sx)),],
               z = true_sx[-which(is.nan(true_sx))]))
p3 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sy)),],
               z = true_sy[-which(is.nan(true_sy))]))
p4 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sxx)),],
               z = true_sxx[-which(is.nan(true_sxx))]))
p5 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sxy)),],
               z = true_sxy[-which(is.nan(true_sxy))]))
p6 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_syy)),],
               z = true_syy[-which(is.nan(true_syy))]))

((p1 + p2 + p3)/(p4 + p5 + p6))

##########################
# Fit a Gaussian Process #
##########################
# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
# Posterior samples for Z(s) and beta
model = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern2")
###################
# Rates of Change #
###################
gradients = sprates(grid = grid,
                    coords = coords,
                    model = model,
                    kernel = "matern2")
p8 = sp_ggplot(data_frame = data.frame(grid,
                z = gradients$estimate.sx[,"50%"],
               sig = gradients$estimate.sx$sig))
p9 = sp_ggplot(data_frame = data.frame(grid,
               z = gradients$estimate.sy[,"50%"],
               sig = gradients$estimate.sy$sig))
p10 = sp_ggplot(data_frame = data.frame(grid,
                 z = gradients$estimate.sxx[,"50%"],
                sig = gradients$estimate.sxx$sig))
p11 = sp_ggplot(data_frame = data.frame(grid,
                 z = gradients$estimate.sxy[,"50%"],
                 sig = gradients$estimate.sxy$sig))
p12 = sp_ggplot(data_frame = data.frame(grid,
                z = gradients$estimate.syy[,"50%"],
                sig = gradients$estimate.syy$sig))

# compare with true estimates
((p7 + p8 + p9)/(p10 + p11 + p12))

## End(Not run)
## Not run: 
require(nimble)
require(nimblewomble)
require(patchwork)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

# Create equally spaced grid of points
xsplit = ysplit = seq(-10, 10, by = 1)[-c(1, 21)]
grid = as.matrix(expand.grid(xsplit, ysplit), ncol = 2)
colnames(grid) = c("x", "y")

####################################
# Process for True Rates of Change #
####################################
# Gradient along x
true_sx = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                grid[,1]/sqrt(grid[,1]^2 + grid[,2]^2), 3)
# Gradient along y
true_sy = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                grid[,2]/sqrt(grid[,1]^2 + grid[,2]^2), 3)
# Curvature along x
true_sxx = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2))/
                  sqrt(grid[,1]^2 + grid[,2]^2) -
                 20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,1]^2/(grid[,1]^2 + grid[,2]^2)^(3/2) -
                 20 * sin(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,1]^2/(grid[,1]^2 + grid[,2]^2), 3)
# Mixed Curvature
true_sxy = round(-20 * (cos(sqrt(grid[,1]^2 + grid[,2]^2)) -
                 sin(sqrt(grid[,1]^2 + grid[,2]^2))) * grid[,1]
                  * grid[,2]/(grid[,1]^2 + grid[,2]^2), 3)
# Curvature along y
true_syy = round(20 * cos(sqrt(grid[,1]^2 + grid[,2]^2))/
                  sqrt(grid[,1]^2 + grid[,2]^2) -
                 20 * cos(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,2]^2/(grid[,1]^2 + grid[,2]^2)^(3/2) -
                 20 * sin(sqrt(grid[,1]^2 + grid[,2]^2)) *
                 grid[,2]^2/(grid[,1]^2 + grid[,2]^2), 3)
# Create the plots
p1 = sp_ggplot(data_frame = data.frame(coords, z = y))
p2 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sx)),],
               z = true_sx[-which(is.nan(true_sx))]))
p3 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sy)),],
               z = true_sy[-which(is.nan(true_sy))]))
p4 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sxx)),],
               z = true_sxx[-which(is.nan(true_sxx))]))
p5 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_sxy)),],
               z = true_sxy[-which(is.nan(true_sxy))]))
p6 = sp_ggplot(data_frame = data.frame(grid[-which(is.nan(true_syy)),],
               z = true_syy[-which(is.nan(true_syy))]))

((p1 + p2 + p3)/(p4 + p5 + p6))

##########################
# Fit a Gaussian Process #
##########################
# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
# Posterior samples for Z(s) and beta
model = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern2")
###################
# Rates of Change #
###################
gradients = sprates(grid = grid,
                    coords = coords,
                    model = model,
                    kernel = "matern2")
p8 = sp_ggplot(data_frame = data.frame(grid,
                z = gradients$estimate.sx[,"50%"],
               sig = gradients$estimate.sx$sig))
p9 = sp_ggplot(data_frame = data.frame(grid,
               z = gradients$estimate.sy[,"50%"],
               sig = gradients$estimate.sy$sig))
p10 = sp_ggplot(data_frame = data.frame(grid,
                 z = gradients$estimate.sxx[,"50%"],
                sig = gradients$estimate.sxx$sig))
p11 = sp_ggplot(data_frame = data.frame(grid,
                 z = gradients$estimate.sxy[,"50%"],
                 sig = gradients$estimate.sxy$sig))
p12 = sp_ggplot(data_frame = data.frame(grid,
                z = gradients$estimate.syy[,"50%"],
                sig = gradients$estimate.syy$sig))

# compare with true estimates
((p7 + p8 + p9)/(p10 + p11 + p12))

## End(Not run)

Posterior samples for wombling measures

Description

Posterior samples for wombling measures

Usage

spwombling(
  coords = NULL,
  curve = NULL,
  model = NULL,
  kernel = c("matern1", "matern2", "gaussian")
)
spwombling(
  coords = NULL,
  curve = NULL,
  model = NULL,
  kernel = c("matern1", "matern2", "gaussian")
)

Arguments

coords

coordinates

curve

coordinates of the curve for wombling

model

posterior samples of $Z(s)$ , $\phi$ , $\sigma^2$

kernel

choice of kernel; must be one of "matern1", "matern2", "gaussian"

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
require(nimble)
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

# Create equally spaced grid of points
xsplit = ysplit = seq(-10, 10, by = 1)[-c(1, 21)]
grid = as.matrix(expand.grid(xsplit, ysplit), ncol = 2)
colnames(grid) = c("x", "y")

##########################
# Fit a Gaussian Process #
##########################
# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
# Posterior samples for Z(s) and beta
model = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern2")
############
# Wombling #
############
# Pick any curve (contour) of your choice
# curve = your contour
tvec = sapply(1:(nrow(curve) - 1), function(x) sqrt(sum((curve[(x + 1),] - curve[x,])^2)))
umat = as.matrix(t(sapply(1:(nrow(curve) - 1), function(x) (curve[(x + 1),] - curve[x,])))/tvec)

wm = spwombling(coords = coords,
                curve = curve,
                model = model,
                kernel = "matern2")

# Total wombling measure for gradient
colSums(wm$estimate.wm.1[,-4]); colSums(wm$estimate.wm.1[,-4])/sum(tvec)
# Total wombling measure for curvature
colSums(wm$estimate.wm.2[,-4]); colSums(wm$estimate.wm.2[,-4])/sum(tvec)


# Color code points based on significance
col.pts.1 = sapply(wm$estimate.wm.1$sig, function(x){
  if(x == 1) return("green")
  else if(x == -1) return("cyan")
  else return(NA)
  })

  col.pts.2 = sapply(wm$estimate.wm.2$sig, function(x){
  if(x == 1) return("green")
  else if(x == -1) return("cyan")
  else return(NA)
  })

p13 = sp_ggplot(data_frame = data.frame(coords, y))
p14 = p13 + geom_path(curve, mapping = aes(x, y), linewidth = 2)
p15 = p13 + geom_path(curve, mapping = aes(x, y), linewidth = 2) +
geom_path(curve, mapping = aes(x, y),
          colour = c(col.pts.1, NA), linewidth = 1, na.rm = TRUE)
p16 = p13 + geom_path(curve, mapping = aes(x, y), linewidth = 2) +
            geom_path(curve, mapping = aes(x, y),
            colour = c(col.pts.2, NA), linewidth = 1, na.rm = TRUE)

p14 + (p15/p16)

###############
# True Values #
###############
truth = matrix(0, nrow = nrow(curve) - 1, ncol = 2)
rule = seq(0, 1, by = 0.01)

for(i in 1:(nrow(curve) - 1)){
  u.perp = c(umat[i, 2], - umat[i, 1])
  s0 = curve[i,]

truth.lsegment = sapply(rule * tvec[i], function(x){
  s.t = s0 + x * umat[i,]
  true_sx = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[1]/
            sqrt(s.t[1]^2 + s.t[2]^2)
  true_sy = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[2]/
            sqrt(s.t[1]^2 + s.t[2]^2)
  true_sx * u.perp[1] + true_sy * u.perp[2]
  })

truth[i, 1] = sum(truth.lsegment * (tvec[i]/101))

truth.lsegment = sapply(rule * tvec[i], function(x){
  s.t = s0 + x * umat[i,]
  true_sxx = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2))/sqrt(s.t[1]^2 + s.t[2]^2) -
    20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) *
            s.t[1]^2/(s.t[1]^2 + s.t[2]^2)^(3/2) -
    20 * sin(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[1]^2/(s.t[1]^2 + s.t[2]^2)
  true_sxy = -20 * (cos(sqrt(s.t[1]^2 + s.t[2]^2)) -
                sin(sqrt(s.t[1]^2 + s.t[2]^2))) *
                      s.t[1] * s.t[2]/(s.t[1]^2 + s.t[2]^2)
  true_syy = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2))/sqrt(s.t[1]^2 + s.t[2]^2) -
    20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) *
               s.t[2]^2/(s.t[1]^2 + s.t[2]^2)^(3/2) -
    20 * sin(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[2]^2/(s.t[1]^2 + s.t[2]^2)
  true_sxx * u.perp[1]^2 + 2 * true_sxy * u.perp[1] * u.perp[2] +
                true_syy * u.perp[2]^2
  })
  truth[i, 2] = sum(truth.lsegment * (tvec[i]/101))
}
true.total = colSums(truth); true.total
true.avg.total = true.total/sum(tvec); true.avg.total

## End(Not run)
## Not run: 
require(nimble)
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

# Create equally spaced grid of points
xsplit = ysplit = seq(-10, 10, by = 1)[-c(1, 21)]
grid = as.matrix(expand.grid(xsplit, ysplit), ncol = 2)
colnames(grid) = c("x", "y")

##########################
# Fit a Gaussian Process #
##########################
# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
# Posterior samples for Z(s) and beta
model = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern2")
############
# Wombling #
############
# Pick any curve (contour) of your choice
# curve = your contour
tvec = sapply(1:(nrow(curve) - 1), function(x) sqrt(sum((curve[(x + 1),] - curve[x,])^2)))
umat = as.matrix(t(sapply(1:(nrow(curve) - 1), function(x) (curve[(x + 1),] - curve[x,])))/tvec)

wm = spwombling(coords = coords,
                curve = curve,
                model = model,
                kernel = "matern2")

# Total wombling measure for gradient
colSums(wm$estimate.wm.1[,-4]); colSums(wm$estimate.wm.1[,-4])/sum(tvec)
# Total wombling measure for curvature
colSums(wm$estimate.wm.2[,-4]); colSums(wm$estimate.wm.2[,-4])/sum(tvec)


# Color code points based on significance
col.pts.1 = sapply(wm$estimate.wm.1$sig, function(x){
  if(x == 1) return("green")
  else if(x == -1) return("cyan")
  else return(NA)
  })

  col.pts.2 = sapply(wm$estimate.wm.2$sig, function(x){
  if(x == 1) return("green")
  else if(x == -1) return("cyan")
  else return(NA)
  })

p13 = sp_ggplot(data_frame = data.frame(coords, y))
p14 = p13 + geom_path(curve, mapping = aes(x, y), linewidth = 2)
p15 = p13 + geom_path(curve, mapping = aes(x, y), linewidth = 2) +
geom_path(curve, mapping = aes(x, y),
          colour = c(col.pts.1, NA), linewidth = 1, na.rm = TRUE)
p16 = p13 + geom_path(curve, mapping = aes(x, y), linewidth = 2) +
            geom_path(curve, mapping = aes(x, y),
            colour = c(col.pts.2, NA), linewidth = 1, na.rm = TRUE)

p14 + (p15/p16)

###############
# True Values #
###############
truth = matrix(0, nrow = nrow(curve) - 1, ncol = 2)
rule = seq(0, 1, by = 0.01)

for(i in 1:(nrow(curve) - 1)){
  u.perp = c(umat[i, 2], - umat[i, 1])
  s0 = curve[i,]

truth.lsegment = sapply(rule * tvec[i], function(x){
  s.t = s0 + x * umat[i,]
  true_sx = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[1]/
            sqrt(s.t[1]^2 + s.t[2]^2)
  true_sy = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[2]/
            sqrt(s.t[1]^2 + s.t[2]^2)
  true_sx * u.perp[1] + true_sy * u.perp[2]
  })

truth[i, 1] = sum(truth.lsegment * (tvec[i]/101))

truth.lsegment = sapply(rule * tvec[i], function(x){
  s.t = s0 + x * umat[i,]
  true_sxx = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2))/sqrt(s.t[1]^2 + s.t[2]^2) -
    20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) *
            s.t[1]^2/(s.t[1]^2 + s.t[2]^2)^(3/2) -
    20 * sin(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[1]^2/(s.t[1]^2 + s.t[2]^2)
  true_sxy = -20 * (cos(sqrt(s.t[1]^2 + s.t[2]^2)) -
                sin(sqrt(s.t[1]^2 + s.t[2]^2))) *
                      s.t[1] * s.t[2]/(s.t[1]^2 + s.t[2]^2)
  true_syy = 20 * cos(sqrt(s.t[1]^2 + s.t[2]^2))/sqrt(s.t[1]^2 + s.t[2]^2) -
    20 * cos(sqrt(s.t[1]^2 + s.t[2]^2)) *
               s.t[2]^2/(s.t[1]^2 + s.t[2]^2)^(3/2) -
    20 * sin(sqrt(s.t[1]^2 + s.t[2]^2)) * s.t[2]^2/(s.t[1]^2 + s.t[2]^2)
  true_sxx * u.perp[1]^2 + 2 * true_sxy * u.perp[1] * u.perp[2] +
                true_syy * u.perp[2]^2
  })
  truth[i, 2] = sum(truth.lsegment * (tvec[i]/101))
}
true.total = colSums(truth); true.total
true.avg.total = true.total/sum(tvec); true.avg.total

## End(Not run)

Posterior samples for wombling measures for the squared exponential kernel

Description

For internal use only.

Usage

wombling_gaussian(coords, curve, dists, tvec, umat, z, phi, sigma2)
wombling_gaussian(coords, curve, dists, tvec, umat, z, phi, sigma2)

Arguments

coords

coordinates

curve

curve coordinates

dists

distance matrix

tvec

vector of t's

umat

matrix of u's

z

posterior samples of $Z(s)$

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
WG = compileNimble(wombling_gaussian)
wmeasure = WG(coords = coords,
              curve = curve,
              dists = distM,
              tvec = tvec,
              umat = umat,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
WG = compileNimble(wombling_gaussian)
wmeasure = WG(coords = coords,
              curve = curve,
              dists = distM,
              tvec = tvec,
              umat = umat,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)

Posterior samples for wombling measures from the Matern kernel with $\nu=3/2$

Description

For internal use only.

Usage

wombling_matern1(coords, curve, dists, tvec, umat, z, phi, sigma2)
wombling_matern1(coords, curve, dists, tvec, umat, z, phi, sigma2)

Arguments

coords

coordinates

curve

curve coordinates

dists

distance matrix

tvec

vector of t's

umat

matrix of u's

z

posterior samples of $Z(s)$

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
WM1 = compileNimble(wombling_matern1)
wmeasure = WM1(coords = coords,
              curve = curve,
              dists = distM,
              tvec = tvec,
              umat = umat,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
WM1 = compileNimble(wombling_matern1)
wmeasure = WM1(coords = coords,
              curve = curve,
              dists = distM,
              tvec = tvec,
              umat = umat,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)

Posterior samples for wombling measures from the Matern kernel with $\nu=5/2$

Description

For internal use only.

Usage

wombling_matern2(coords, curve, dists, tvec, umat, z, phi, sigma2)
wombling_matern2(coords, curve, dists, tvec, umat, z, phi, sigma2)

Arguments

coords

coordinates

curve

curve coordinates

dists

distance matrix

tvec

vector of t's

umat

matrix of u's

z

posterior samples of $Z(s)$

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
WM2 = compileNimble(wombling_matern2)
wmeasure = WM2(coords = coords,
              curve = curve,
              dists = distM,
              tvec = tvec,
              umat = umat,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::spwombling(...)
WM2 = compileNimble(wombling_matern2)
wmeasure = WM2(coords = coords,
              curve = curve,
              dists = distM,
              tvec = tvec,
              umat = umat,
              z = z,
              phi = phi,
              sigma2 = sigma2)

## End(Not run)

Posterior samples of spatial effects and intercept for the squared exponential kernel

Description

For internal use only.

Usage

zbeta_gaussian(y, dists, phi, sigma2, tau2)
zbeta_gaussian(y, dists, phi, sigma2, tau2)

Arguments

y

response

dists

distance matrix derived from coordinates

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

tau2

posterior samples of $\tau^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zbG = compileNimble(zbeta_gaussian)
zb.samples = zbG(y = y, dists = dists, phi = phi, sigma2 = sigma2,
                 tau2 = tau2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zbG = compileNimble(zbeta_gaussian)
zb.samples = zbG(y = y, dists = dists, phi = phi, sigma2 = sigma2,
                 tau2 = tau2)

## End(Not run)

Posterior samples of spatial effects and intercept for the Matern kernel with $nu=3/2$

Description

For internal use only.

Usage

zbeta_matern1(y, dists, phi, sigma2, tau2)
zbeta_matern1(y, dists, phi, sigma2, tau2)

Arguments

y

response

dists

distance matrix derived from coordinates

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

tau2

posterior samples of $\tau^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zbM1 = compileNimble(zbeta_matern1)
zb.samples = zbM1(y = y, dists = dists, phi = phi, sigma2 = sigma2,
                 tau2 = tau2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zbM1 = compileNimble(zbeta_matern1)
zb.samples = zbM1(y = y, dists = dists, phi = phi, sigma2 = sigma2,
                 tau2 = tau2)

## End(Not run)

Posterior samples of spatial effects and intercept for the Matern kernel with $\nu=5/2$

Description

For internal use only.

Usage

zbeta_matern2(y, dists, phi, sigma2, tau2)
zbeta_matern2(y, dists, phi, sigma2, tau2)

Arguments

y

response

dists

distance matrix derived from coordinates

phi

posterior samples of $\phi$

sigma2

posterior samples of $\sigma^2$

tau2

posterior samples of $\tau^2$

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zbM2 = compileNimble(zbeta_matern2)
zb.samples = zbM2(y = y, dists = dists, phi = phi, sigma2 = sigma2,
                 tau2 = tau2)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zbM2 = compileNimble(zbeta_matern2)
zb.samples = zbM2(y = y, dists = dists, phi = phi, sigma2 = sigma2,
                 tau2 = tau2)

## End(Not run)

Posterior samples of spatial effects and intercept for Matern with $nu=3/2$

Description

For internal use only.

Usage

zbeta_samples(
  coords = NULL,
  y = NULL,
  X = NULL,
  model = NULL,
  kernel = c("matern1", "matern2", "gaussian")
)
zbeta_samples(
  coords = NULL,
  y = NULL,
  X = NULL,
  model = NULL,
  kernel = c("matern1", "matern2", "gaussian")
)

Arguments

coords

coordinates

y

response

X

covariates (supply as a matrix without intercept)

model

matrix of posterior samples of $\phi$ , $\sigma^2$ and $\tau^2$

kernel

choice of kernel; must be one of "matern1", "matern2", "gaussian"

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
require(nimble)
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
# Posterior samples for Z(s) and beta
model = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern2")
estimates = t(round(apply(model, 2, quantile, probs = c(0.5, 0.025, 0.975)), 3))
yfit = estimates[paste("z", 1:N, sep = ""), "50%"] + estimates["b0", "50%"]
ylow = estimates[paste("z", 1:N, sep = ""), "2.5%"] + estimates["b0", "2.5%"]
yhigh = estimates[paste("z", 1:N, sep = ""), "97.5%"] + estimates["b0", "97.5%"]
fit_frame = cbind(true = y, est = yfit, `2.5%` = ylow, `97.5%` = yhigh)
fit_frame$sig = significance(data_frame = data.frame(fit_frame[,-1]))

# Plot
sp_ggplot(data_frame = data.frame(coords, z = yfit, sig = fit_frame$sig))

## End(Not run)
## Not run: 
require(nimble)
require(nimblewomble)

set.seed(1)
# Generated Simulated Data
N = 1e2
tau = 1
coords = matrix(runif(2 * N, -10, 10), ncol = 2); colnames(coords) = c("x", "y")
y = rnorm(N, mean = 20 * sin(sqrt(coords[, 1]^2  + coords[, 2]^2)), sd = tau)

# Posterior samples for theta
mc_sp = gp_fit(coords = coords, y = y, kernel = "matern2")
# Posterior samples for Z(s) and beta
model = zbeta_samples(y = y, coords = coords,
                      model = mc_sp$mcmc,
                      kernel = "matern2")
estimates = t(round(apply(model, 2, quantile, probs = c(0.5, 0.025, 0.975)), 3))
yfit = estimates[paste("z", 1:N, sep = ""), "50%"] + estimates["b0", "50%"]
ylow = estimates[paste("z", 1:N, sep = ""), "2.5%"] + estimates["b0", "2.5%"]
yhigh = estimates[paste("z", 1:N, sep = ""), "97.5%"] + estimates["b0", "97.5%"]
fit_frame = cbind(true = y, est = yfit, `2.5%` = ylow, `97.5%` = yhigh)
fit_frame$sig = significance(data_frame = data.frame(fit_frame[,-1]))

# Plot
sp_ggplot(data_frame = data.frame(coords, z = yfit, sig = fit_frame$sig))

## End(Not run)

Posterior samples of spatial effects and intercept for all kernels in the presence of covariates

Description

For internal use only.

Usage

zXbeta(y, X, beta)
zXbeta(y, X, beta)

Arguments

y

response

X

covariates (supply as a matrix without intercept)

beta

posterior samples of $\beta$ (supply as a matrix)

Author(s)

Aritra Halder <[email protected]>,
Sudipto Banerjee <[email protected]>

Examples

## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zXb = nimble::compileNimble(zXbeta)
zb.samples = zXb(y = y, X = X, beta = beta)

## End(Not run)
## Not run: 
#####################
# Internal use only #
#####################
# Example usage inside of nimblewomble::zbeta_samples(...)
zXb = nimble::compileNimble(zXbeta)
zb.samples = zXb(y = y, X = X, beta = beta)

## End(Not run)

Package 'nimblewomble'

Help Index

Posterior samples of rates of change (gradients and curvatures) for the Matern kernel with ν→∞\nu\to\inftyν→∞ producing the squared exponential kernel.

Description

Usage

Arguments

Author(s)

Examples

Posterior samples of rates of change (gradients and curvatures) for the Matern kernel with ν=5/2\nu=5/2ν=5/2

Description

Usage

Arguments

Author(s)

Examples

Incomplete Gamma Function

Description

Usage

Arguments

Author(s)

Examples

Cross-covariance terms for the posterior distribution of wombling measures for Matern ν=3/2\nu=3/2ν=3/2.

Description

Usage

Arguments

Author(s)

Examples

Cross-covariance terms for the posterior distribution of wombling measures for Matern ν→∞\nu\to\inftyν→∞, the squared exponential kernel.

Description

Usage

Arguments

Author(s)

Examples

Cross-covariance terms for the posterior distribution of wombling measures for Matern ν=5/2\nu=5/2ν=5/2, the squared exponential kernel.

Description

Usage

Arguments

Author(s)

Examples

Squared Exponential Covariance kernel

Description

Usage

Arguments

Author(s)

Examples

Fit a Gaussian process

Description

Usage

Arguments

Author(s)

Examples

Posterior samples of rates of change (gradients) for the Matern kernel with ν=3/2\nu=3/2ν=3/2

Description

Usage

Arguments

Author(s)

Examples

Matern Covariance kernel with ν=3/2\nu = 3/2ν=3/2

Description

Usage

Arguments

Author(s)

Examples

Matern Covariance kernel with ν=5/2\nu = 5/2ν=5/2

Description

Usage

Arguments

Author(s)

Examples

Computes the Cumulative Distribution Function for the standard Gaussian probability distribution

Description

Usage

Arguments

Author(s)

Examples

Determines significance for posterior estimates

Description

Usage

Arguments

Author(s)

Examples

Posterior samples of rates of change (gradients and curvatures) for the Matern kernel with $\nu\to\infty$ producing the squared exponential kernel.

Posterior samples of rates of change (gradients and curvatures) for the Matern kernel with $\nu=5/2$

Cross-covariance terms for the posterior distribution of wombling measures for Matern $\nu=3/2$ .

Cross-covariance terms for the posterior distribution of wombling measures for Matern $\nu\to\infty$ , the squared exponential kernel.

Cross-covariance terms for the posterior distribution of wombling measures for Matern $\nu=5/2$ , the squared exponential kernel.

Posterior samples of rates of change (gradients) for the Matern kernel with $\nu=3/2$

Matern Covariance kernel with $\nu = 3/2$

Matern Covariance kernel with $\nu = 5/2$

Posterior samples for wombling measures from the Matern kernel with $\nu=3/2$

Posterior samples for wombling measures from the Matern kernel with $\nu=5/2$

Posterior samples of spatial effects and intercept for the Matern kernel with $nu=3/2$

Posterior samples of spatial effects and intercept for the Matern kernel with $\nu=5/2$

Posterior samples of spatial effects and intercept for Matern with $nu=3/2$