print_outputs {binspp}

Text output describing the posterior distributions

Description

The summaries of the posterior distributions in the text form are provided.

Usage

print_outputs(Output)

Arguments

Output

list, output of the main function estintp().

Details

The parameter estimates (sample medians from the empirical posterior distributions) and the 2.5% and 97.5% quantiles from the empirical posterior distributions are printed.
Additionally, during the run of the MCMC chain the significance of the covariates in the list z_beta with respect to the current population of parent points is repeatedly tested. This function prints the medians of the series of p-values obtained in this way, together with the corresponding 2.5% and 97.5% sample quantiles of the p-values for each covariate.

Value

Text output summarizing the posterior distributions.

Examples

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)
z_alpha = list(tmi = cov_tmi, tdensity = cov_tdensity)
z_omega = list(slope = cov_slope, reserv = cov_reserv)

# 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)


# Default parameters for prior distributions:
control = list(NStep = 100, BurnIn = 20, SamplingFreq = 5)


# MCMC estimation:
Output = estintp(X, control, x_left, x_right, y_bottom, y_top, W_dil,
                 z_beta, z_alpha, z_omega, verbose = FALSE)


# Text output
print_outputs(Output)