nb_pi {predint}

Simple uncalibrated prediction intervals for negative-binomial data

Description

nb_pi() is a helper function that is internally called by neg_bin_pi(). It calculates simple uncalibrated prediction intervals for negative-binomial data with offsets.

Usage

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

Arguments

newoffset

number of experimental units in the future clusters

histoffset

number of experimental units in the historical clusters

lambda

overall Poisson mean

kappa

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 \hat{\lambda} \pm q \sqrt{n^*_m \frac{\hat{\lambda} + \hat{\kappa} \bar{n} \hat{\lambda}}{\bar{n} H} + (n^*_m \hat{\lambda} + \hat{\kappa} n^{*2}_m \hat{\lambda}^2) }

with n^*_m as the number of experimental units in m=1, 2, ... , M future clusters, \hat{\lambda} as the estimate for the Poisson mean obtained from the historical data, \hat{\kappa} as the estimate for the dispersion parameter, n_h as the number of experimental units per historical cluster and \bar{n}=\sum_h^{n_h} n_h / H.

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

Value

np_pi returns an object of class c("predint", "negativeBinomialPI").

Examples

# Prediction interval
nb_pred <- nb_pi(newoffset=3, lambda=3, kappa=0.04, histoffset=1:9, q=qnorm(1-0.05/2))
summary(nb_pred)