Maintainer: Luca Sartore
Prediction limits for Poisson distribution are useful when predicting the occurrences of some real life phenomena; in fact, these limits quantify the uncertainty associated with the predicted values. The plpoisson package provides a set of functions to compute prediction limits of the inferred Poisson distribution under both frequentist and Bayesian frameworks.
For a complete list of exported functions, use
library(help = "plpoisson") once the plpoisson package is installed (see the
inst/INSTALL.md file for a detailed description of the setup process).
## Loading the package library(plpoisson) ## Setting quantities of interest xobs <- rpois(1, 50) # Number of the observed occurrencies n <- 1 # Total number of the time windows of # of size 's' observed in the past s <- rgamma(1, 4, .567) # Fixed size of observed time windows t <- rgamma(1, 3, .33) # Future time window a <- 5 # Shape hyperparameter of a gamma prior b <- 1.558 # Rate hyperparameter of a gamma prior ## Frequentist prediction limits poiss(xobs, n, s, t) ## Bayesian prediction limits (with uniform prior) poisUNIF(xobs, n, s, t) ## Bayesian prediction limits (with Jeffreys prior) poisJEFF(xobs, n, s, t) ## Bayesian prediction limits (with gamma prior) poisBayes(xobs, n, s, t, a, b)
Bejleri, V. (2005). Bayesian Prediction Intervals for the Poisson Model, Noninformative Priors, Ph.D. Dissertation, American University, Washington, DC.
Bejleri, V., & Nandram, B. (2018). Bayesian and frequentist prediction limits for the Poisson distribution. Communications in Statistics-Theory and Methods, 47(17), 4254-4271.
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