estgtpr {binspp} R Documentation

## 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)


### 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.

### Value

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

### 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 = "")