Depending on the current transformation \(h(y)= \{y, \sqrt{y}, y^{2/3}\}\),
$$V(h(y_0)-h(\mu_0))=V(h(y_0))+V(h(\mu_0))$$
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.
algo.farrington.threshold(pred,phi,alpha=0.01,skewness.transform="none",y)
Vector of length four with lower and upper bounds of an
\((1-\alpha)\cdot 100\%\) confidence interval (first two
arguments) and corresponding quantile of observation y
together with the median of the predictive distribution.
A GLM prediction object
Current overdispersion parameter (superfluous?)
Quantile level in Gaussian based CI, i.e. an \((1-\alpha)\cdot 100\%\) confidence interval is computed.
Skewness correction, i.e. one of
"none"
, "1/2"
, or "2/3"
.
Observed number