plot_Posteriors {poolABC}

Plot multiple posterior distributions

Description

Plots, in the same plot, the density of multiple posterior distributions of a given parameter.

Usage

plot_Posteriors(posteriors, index, limits, weights = NULL)

Arguments

posteriors

is a list with samples from the posterior distributions obtained for each target. Each entry of the list is a matrix where each row corresponds to a different accepted simulations and each column corresponds to a different parameter.

index

an non-negative integer indicating which parameter to plot. It corresponds to the desired column of a matrix in the posteriors input. So, to plot the first parameter, corresponding to the first column in the posteriors input select 1. To plot the second parameter, select 2 and so on.

limits

is a matrix with two columns and as many rows as there are parameters. Each row should contain the minimum value of the prior for a given parameter in the first column and the maximum value in the second column.

weights

is an optional list input containing the weights from the local linear regression method. Each entry of the list should be a numeric vector with the weights for each accepted simulation.

Details

After using the multipleABC() or ABC() functions to perform parameter estimation with Approximate Bayesian Computation with several targets, this function can be used for a quick visualization of the quality of an ABC analysis. Multiple posterior distributions, each obtained for a different target, are plotted in the same plot, allowing for a visualization of the shape of the posteriors and a quick inspection of whether all the posteriors converge to the same estimate.

Value

a plot with multiple posterior distributions, each obtained for a different target, for the selected parameter.

Examples

# load the matrix with parameter values
data(params)
# load the matrix with simulated parameter values
data(sumstats)
# load the matrix with the prior limits
data(limits)

# select some random simulations to act as target just to test the function
targets <- sumstats[c(11:20) ,]
# we should remove those random simulation from the sumstats and params matrices
sumstats <- sumstats[-c(11:20), ]; params <- params[-c(11:20), ]

# parameter estimation for a single target
myabc <- multipleABC(targets = targets, params = params, sumstats = sumstats, limits = limits,
tol = 0.01, method = "regression")

# plot multiple posteriors
plot_Posteriors(posteriors = myabc$adjusted, index = 1, limits = limits, weights = myabc$weights)

# note that this is just an example!
# we don't have enough simulations to obtain credible results