CompLogML: Computes Marginal Likelihoods for Each Clique and Value of...
In dga: Capture-Recapture Estimation using Bayesian Model Averaging

Description Usage Arguments Value Author(s) References Examples

Assembles all of the pieces of the marginal likelihoods to be used to calculate the posterior probability of each model/value of Nmissing.

1	CompLogML(D, Nmissing, delta)

`D`	A marginal array of the list overlap counts.
`Nmissing`	The vector of possible values for the missing cell.
`delta`	The prior hyper parameter for the Dirichlet distribution.

The log marginal likelihood of the marginal table.

James Johndrow james.johndrow@gmail.com and Kristian Lum kl@hrdag.org

Madigan, David, and Jeremy C. York. "Bayesian methods for estimation of the size of a closed population." Biometrika 84.1 (1997): 19-31.

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))

# Compute marginal array over lists 1 and 3
D <- apply(Y, c(1, 3), sum)

dga:::CompLogML(D, 1:300, 0.5)