re_estimate {binspp} R Documentation

## Re-estimate the posterior distributions with different burn-in

### Description

After running the MCMC chain for the given number of steps, the trace plots may indicate that 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.

### Usage

re_estimate(Output, BurnIn = 0)


### Arguments

 Output list, output of the main function estintp. BurnIn new value of burn-in.

### 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 simple and quick re-estimation of the posterior distributions with either higher or lower value of burn-in than the one used originally. The output of this function has the same structure as the output of the main function estintp().

### Value

List containing the parameter estimates along with the 2.5% and 97.5% quantiles of the posterior distributions, along with auxiliary objects needed for printing and plotting the outputs.

### 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, x_right), c(y_bottom, y_top))
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 = 50, 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 + series of figures:
print_outputs(Output)
plot_outputs(Output)

# Recompute the outputs when another value of burn-in is desired,
# without running the chain again:
Out2 <- re_estimate(Output, BurnIn = 80)
print_outputs(Out2)
plot_outputs(Out2)



[Package binspp version 0.1.26 Index]