# Compute prediction interval for a new observation

### 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.

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