# Re-estimate the posterior distributions with a different burn-in or a different credibility level. After running the MCMC chain for the given number of steps, the trace plots may indicate that a too small value of burn-in was used in the first place. This function enables re-estimating the posterior distributions with a different value of burn-in, without the need to run the MCMC chain again. Similarly, it allows determining the credible intervals of the posterior distributions with a different credibility level. ```r re_estimate(Output, BurnIn = 0, credibility.level = NULL) ``` ## Arguments - `Output`: list, output of the main function estintp. - `BurnIn`: new value of burn-in. - `credibility.level`: new value of the credibility level. ## Returns List containing the parameter estimates along with the credible intervals of the posterior distributions, along with auxiliary objects needed for printing and plotting the outputs. ## Details The output of the main function binspp contains all the intermediate states of the chain (sampled with the required frequency) no matter what the original value of burn-in was. This enables a simple and quick re-estimation of the posterior distributions with either higher or lower value of burn-in than the one used originally. Similarly, this function can be used to determine the credible intervals of the posterior distributions with a different credibility level. The output of this function has the same structure as the output of the main function `estintp()`. ## Examples ```r 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 = X, control = control, x_left = x_left, x_right = x_right, y_bottom = y_bottom, y_top = y_top, W_dil = W_dil, z_beta = z_beta, z_alpha = z_alpha, z_omega = z_omega, verbose = FALSE) # Text output + series of figures: print(Output) plot(Output) # Recompute the outputs when another value of burn-in is desired, # without running the chain again: Out2 <- re_estimate(Output, BurnIn = 80) print(Out2) plot(Out2) ```

re_estimate function