algo.farrington.threshold: Compute prediction interval for a new observation

In jimhester/surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

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

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.

jimhester/surveillance documentation built on May 19, 2019, 10:33 a.m.