LCMCR (version 0.4.14)

lcmCR: Bayesian Nonparametric Latent Class Capture-Recapture

Description

Create and initialize an object of class lcm_CR_Basic.

Usage

lcmCR(captures, tabular = FALSE, in_list_label = "1", not_in_list_label = "0", 
  K = 5, a_alpha = 0.25, b_alpha = 0.25, buffer_size = 10000, thinning = 10, 
  seed = "auto", verbose = TRUE)

Value

An object of class lcm_CR_Basic initialized and ready to use.

Arguments

captures

input dataset. A data frame with the multiple-recapture data. See 'Details' for input formats.

tabular

a logical value indicating whether or not the data is tabulated. See 'Details'.

in_list_label

factor label that indicates that individual is in list (e.g. 'Yes')

not_in_list_label

factor label that indicates that individual is in not list (e.g. 'No')

K

maximum number of latent classes. Indicates the truncation level of the stick-breaking process.

a_alpha

shape parameter of the prior distribution of concentration parameter of the stick-breaking process.

b_alpha

inverse scale parameter of the prior distribution of concentration parameter of the stick-breaking process.

buffer_size

size of the tracing buffer.

thinning

thinning interval for the tracing buffer

seed

integer seed of the internal RNG.

verbose

Generate progress messages?

Author

Daniel Manrique-Vallier

Details

Input data must be provided as a data frame. The first J columns are two-level factors representing the multiple-recapture lists. Arguments in_list_label and not_in_list_label indicate the labels that represent inclusion and exclusion from the lists. This function supports two input formats:

• When tabular=FALSE each row represents a single individual's capture history. The number of rows must match the size of the observed population. Rows indicating no capture in all list simultaneously are illegal.

• When tabular=TRUE each row represents a unique capture pattern. This format requires an additional numeric column at the right, called "Freq", indicating the count corresponding to such pattern.

See Also

lcm_CR_Basic, lcm_CR_Basic_generator

Examples

require('LCMCR')
data(kosovo_aggregate)
sampler <- lcmCR(captures = kosovo_aggregate, tabular = FALSE, in_list_label = '1',
  not_in_list_label = '0', K = 10, a_alpha = 0.25, b_alpha = 0.25,
  seed = 'auto', buffer_size = 10000, thinning = 100)
sampler
N <- lcmCR_PostSampl(sampler, burnin = 10000, samples = 1000, thinning = 100, output = FALSE)
quantile(N, c(0.025, 0.5, 0.975))