Compute prediction interval for a new observation

Share:

Description

Depending on the current transformation h(y)= \{y, √{y}, y^{2/3}\},

V(h(y_0)-h(μ_0))=V(h(y_0))+V(h(μ_0))

is used to compute a prediction interval. The prediction variance consists of a component due to the variance of having a single observation and a prediction variance.

Usage

1
algo.farrington.threshold(pred,phi,alpha=0.01,skewness.transform="none",y)

Arguments

pred

A GLM prediction object

phi

Current overdispersion parameter (superflous?)

alpha

Quantile level in Gaussian based CI, i.e. an (1-α)\cdot 100\% confidence interval is computed.

skewness.transform

Skewness correction, i.e. one of "none", "1/2", or "2/3".

y

Observed number

Value

Vector of length four with lower and upper bounds of an (1-α)\cdot 100\% confidence interval (first two arguments) and corresponding quantile of observation y together with the median of the predictive distribution.

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.