compute_posterior: Compute the posterior probability distribution of the...

In dupiR: Bayesian Inference from Count Data using Discrete Uniform Priors

Compute the posterior probability distribution of the population size for an object of class Counts

Description

Compute the posterior probability distribution of the population size using a discrete uniform prior and a binomial likelihood ("dup" algorithm, Comoglio et al.). An approximation using a Gamma prior and a Poisson likelihood is used when applicable ("gamma" algorithm) method (see Clough et al. for details)

Usage

compute_posterior(
  object,
  n_start,
  n_end,
  replacement = FALSE,
  b = 1e-10,
  alg = "dup"
)

Arguments

object	object of class Counts
n_start	start of prior support range
n_end	end of prior support range
replacement	was sampling performed with replacement? Default to FALSE
b	prior rate parameter of the gamma distribution used to compute the posterior with Clough. Default to 1e-10
alg	algorithm to be used to compute posterior. One of ... . Default to "dup"

Value

an object of class Counts

Author(s)

Federico Comoglio

References

Comoglio F, Fracchia L and Rinaldi M (2013) Bayesian inference from count data using discrete uniform priors. PLoS ONE 8(10): e74388

Clough HE et al. (2005) Quantifying Uncertainty Associated with Microbial Count Data: A Bayesian Approach. Biometrics 61: 610-616

Examples

counts <- new_counts(counts = c(20,30), fractions = c(0.075, 0.10))

# default parameters ("dup" algorithm, sampling without replacement, default prior support)
posterior <- compute_posterior(counts)

# custom prior support ("dup" algorithm)
posterior <- compute_posterior(counts, n_start = 0, n_end = 1e3)

# gamma prior ("gamma" algorithm)
posterior <- compute_posterior(counts, alg = "gamma")

# sampling with replacement
posterior <- compute_posterior(counts, replacement = TRUE)

dupiR documentation built on May 29, 2024, 1:21 a.m.