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.
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 \((1-\alpha)\cdot 100\%\)
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
\((1-\alpha)\cdot 100\%\) confidence interval (first two
arguments) and corresponding quantile of observation y
together with the median of the predictive distribution.