# GetBMA: Compute the Bayesian Model average In sppmix: Modeling Spatial Poisson and Related Point Processes

## Description

This function uses the posterior realizations from a `est_mix_bdmcmc` call, to compute the Bayesian Model Average across different number of components and returns the fitted Poisson point process with mixture of normals intensity surface.

For examples see

## Usage

 ```1 2``` ```GetBMA(fit, win = fit\$data\$window, burnin = fit\$L/10, LL = 100, zlims = c(0, 0)) ```

## Arguments

 `fit` Object of class `bdmcmc_res`. `win` An object of class `owin`. `burnin` Number of initial realizations to discard. By default, it is 1/10 of the total number of iterations. `LL` Length of the side of the square grid. The density or intensity is calculated on an L * L grid. The larger this value is, the slower the calculation, but the better the approximation as well as the smoother the resulting plots. `zlims` The limits of the z axis. Defaults to [0,max(z)].

## Value

An image as an object of class `im.object`.

## Author(s)

Sakis Micheas

`est_mix_bdmcmc`, `plotmix_3d`, `plot_density`
 ```1 2 3 4 5 6 7 8``` ```fit=est_mix_bdmcmc(pp = spatstat::redwood, m = 5) BMA=GetBMA(fit) burnin=.1*fit\$L title1 = paste("Bayesian model average of",fit\$L-burnin,"posterior realizations") plotmix_3d(BMA,title1=title1) plot_density(as.data.frame(BMA))+ggplot2::ggtitle("Bayesian model average intensity surface") plot_density(as.data.frame(BMA),TRUE)+ggplot2::ggtitle( "Contours of the Bayesian model average intensity surface") ```