Performs capture-recapture estimation by averaging over decomposable graphical models. This implements the approach introduced in Madigan and York (1997).
library(dga)
# Number of lists and prior hyperparameter
p <- 5
data(graphs5) # Decomposable graphical models on 5 lists.
delta <- 0.5
Nmissing <- 1:300 # Reasonable range for the number of unobserved individuals.
# Counts corresponding to list inclusion patterns.
Y <- c(0,27,37,19,4,4,1,1,97,22,37,25,2,1,3,5,83,36,34,18,3,5,0,2,30,5,23,8,0,3,0,2)
Y <- array(Y, dim=c(2,2,2,2,2))
N <- sum(Y) + Nmissing
# Model-wise posterior probaiblities on the total population size.
# weights[i,j] is the posterior probability for j missing individuals under model graphs5[[j]].
weights <- bma.cr(Y, Nmissing, delta, graphs5)
# Plot of the posterior distribution.
plotPosteriorN(weights, N)Written by James Johndrow, Kristian Lum, and Patrick Ball.
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