predint (version 2.2.1)

qp_pi: Simple uncalibrated prediction intervals for quasi-Poisson data

Description

qp_pi() is a helper function that is internally called by quasi_pois_pi(). It calculates simple uncalibrated prediction intervals for Poisson data with constant overdispersion (quasi-Poisson assumption).

Usage

qp_pi(
  newoffset,
  histoffset,
  lambda,
  phi,
  q = qnorm(1 - 0.05/2),
  alternative = "both",
  newdat = NULL,
  histdat = NULL,
  algorithm = NULL
)

Value

qp_pi returns an object of class c("predint", "quasiPoissonPI").

Arguments

newoffset

number of experimental units in the future clusters

histoffset

number of experimental units in the historical clusters

lambda

overall Poisson mean

phi

dispersion parameter

q

quantile used for interval calculation

alternative

either "both", "upper" or "lower" alternative specifies, if a prediction interval or an upper or a lower prediction limit should be computed

newdat

additional argument to specify the current data set

histdat

additional argument to specify the historical data set

algorithm

used to define the algorithm for calibration if called via quasi_pois_pi(). This argument is not of interest for the calculation of simple uncalibrated intervals

Details

This function returns a simple uncalibrated prediction interval $$[l,u{]}_m=n_m^{\ast }\hat{\lambda }\pm q\sqrt{n_m^{\ast }\hat{\varphi }\hat{\lambda }+\frac{n_m^{\ast 2}\hat{\varphi }\hat{\lambda }}{\sum _h{n}_h}}$$

with $n_m^{\ast }$ as the number of experimental units in the $m=1,2,...,M$ future clusters, $\hat{\lambda }$ as the estimate for the Poisson mean obtained from the historical data, $\hat{\varphi }$ as the estimate for the dispersion parameter and $n_h$ as the number of experimental units per historical cluster.

The direct application of this uncalibrated prediction interval to real life data is not recommended. Please use the quasi_pois_pi_pi() functions for real life applications.

Examples

# Prediction interval
qp_pred <- qp_pi(newoffset=3, lambda=3, phi=3, histoffset=1:9, q=qnorm(1-0.05/2))
summary(qp_pred)