print_outputs {binspp}R Documentation


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





list, output of the main function estintp().


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.


Text output summarizing the posterior distributions.


# 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

[Package binspp version 0.1.26 Index]