get_posterior_param: Compute posterior probability distribution parameters (e.g....

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

Compute posterior probability distribution parameters (e.g. credible intervals) for an object of class Counts

Description

This function computes posterior parameters and credible intervals at the given confidence level (default to 95%).

Usage

get_posterior_param(object, low = 0.025, up = 0.975, ...)

Arguments

object	object of class Counts
low	1 - right tail posterior probability
up	left tail posterior probability
...	additional parameters to be passed to plot_posterior

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)

get_posterior_param(posterior)

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