plotPosteriorN: Plots Posterior Distribution of Nmissing

In dga: Capture-Recapture Estimation using Bayesian Model Averaging

Description

Plots the model averaged posterior distribution of the total number of elements (the solid line) and the contribution to the posterior of each of the models (dotted lines)

Usage

plotPosteriorN(weights, N, main = NULL)

Arguments

weights	The output of BMAfunction.
N	N + Nmissing. Or, if you prefer, just Nmissing. The former shows the posterior distribution of the total population size; the latter shows the posterior distribution of the number of missing elements.
main	the title of the plot

Value

A plot.

Author(s)

Kristian Lum [e-mail]

Examples

##### 5 list example from M & Y #######

delta <- .5
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))
Nmissing <- 1:300
N <- Nmissing + sum(Y)
data(graphs5)
weights <- bma.cr(Y, Nmissing, delta, graphs5)
plotPosteriorN(weights, N)


##### 3 list example from M & Y #######
Y <- c(0, 60, 49, 4, 247, 112, 142, 12)
Y <- array(Y, dim = c(2, 2, 2))

delta <- 1
a <- 13.14
b <- 55.17


Nmissing <- 1:300
N <- Nmissing + sum(Y)

logprior <- N * log(b) - (N + a) * log(1 + b) + lgamma(N + a) - lgamma(N + 1) - lgamma(a)

data(graphs3)
weights <- bma.cr(Y, Nmissing, delta, graphs3, logprior)
plotPosteriorN(weights, N)

dga documentation built on May 10, 2021, 5:06 p.m.