binspp (version 0.2.3)

estgtpr: Results for Bayesian MCMC estimation of parameters of generalized Thomas process

Description

Calculates median values for kappa, omega, lambda, theta; calculates 2.5 and 97.5 quantile and draws trace plots.

Usage

estgtpr(est, discard = 100, step = 10)

Value

Median and quantile values and plots (kappa, omega, lambda, theta).

Arguments

est

Output from estgtp() function.

discard

Number of iterations to be discarded as burn in for the estimation.

step

Every step iteration is taken in the parameter estimation.

Examples

library(spatstat)
kappa = 10
omega = .1
lambda= .5
theta = 10

X = rgtp(kappa, omega, lambda, theta, win = owin(c(0, 1), c(0, 1)))
plot(X$X)
plot(X$C)

a_kappa = 4
b_kappa = 1
x <- seq(0, 100, length = 100)
hx <- dlnorm(x, a_kappa, b_kappa)
plot(x, hx, type = "l", lty = 1, xlab = "x value",
     ylab = "Density", main = "Prior")

a_omega = -3
b_omega = 1
x <- seq(0, 1, length = 100)
hx <- dlnorm(x, a_omega, b_omega)
plot(x, hx, type = "l", lty = 1, xlab = "x value",
     ylab = "Density", main = "Prior")

l_lambda = -1
u_lambda = 0.99
x <- seq(-1, 1, length = 100)

hx <- dunif(x, l_lambda, u_lambda)
plot(x, hx, type = "l", lty = 1, xlab = "x value",
     ylab = "Density", main = "Prior")

a_theta = 4
b_theta = 1
x <- seq(0, 100, length = 100)
hx <- dlnorm(x, a_theta, b_theta)
plot(x, hx, type = "l", lty = 1, xlab = "x value",
     ylab = "Density", main = "Prior")

est = estgtp(X$X,
          skappa = exp(a_kappa + ((b_kappa ^ 2) / 2)) / 100,
          somega = exp(a_omega + ((b_omega ^ 2) / 2)) / 100,
          dlambda = 0.01,
          stheta = exp(a_theta + ((b_theta ^ 2) / 2)) / 100, smove = 0.1,
          a_kappa = a_kappa, b_kappa = b_kappa,
          a_omega = a_omega, b_omega = b_omega,
          l_lambda = l_lambda, u_lambda = u_lambda,
          a_theta = a_theta, b_theta = b_theta,
          iter = 50, plot.step = 50, save.step = 1e9,
          filename = "")

discard = 10
step = 10

result = estgtpr(est, discard, step)