first_step {binspp} | R Documentation |
For exploratory purposes it may be useful to perform the first step of the analysis only, to investigate the dependence of the intensity function of the parent process on given covariates, without running the MCMC chain.
first_step(X, z_beta, W_dil, plot = TRUE)
X |
observed point pattern in the |
z_beta |
list of covariates describing the intensity function of the parent process, each covariate being a pixel image as used in the spatstat package. |
W_dil |
the observation window dilated by the assumed maximal cluster radius. |
plot |
logical, should the estimates intensity function of the parent process be plotted? |
The calling the spatstat.model::ppm()
function from the spatstat
package, with some additional computations useful when preparing
the run of the MCMC chain, is mainly performed in this function.
The function also contains a simple way to plot the estimated
intensity function of the parent process.
List containing the output of the spatstat.model::ppm()
function from the spatstat package, along with some auxiliary
objects useful for running the MCMC chain.
library(spatstat)
# Prepare the dataset:
X = trees_N4
x_left = x_left_N4
x_right = x_right_N4
y_bottom = y_bottom_N4
y_top = y_top_N4
z_beta = list(refor = cov_refor, slope = cov_slope)
# Determine the union of rectangles:
W = owin(c(x_left[1], x_right[1]), c(y_bottom[1], y_top[1]))
if (length(x_left) >= 2){
for (i in 2:length(x_left)){
W2 = owin(c(x_left[i], x_right[i]), c(y_bottom[i], y_top[i]))
W = union.owin(W, W2)
}
}
# Dilated observation window:
W_dil = dilation.owin(W, 100)
# Estimating the intensity function of the parent process:
aux = first_step(X, z_beta, W_dil, plot = TRUE)