Compute return levels and confidence limits for a "Renouv" object

Compute return levels and confidence limits for an object of class "Renouv".

Usage

# S3 method for Renouv
predict(object,
        newdata = c(10, 20, 50, 100, 200, 500, 1000),
        cov.rate = TRUE,
        level = c(0.95, 0.7),
        prob = FALSE,
        trace = 1, eps = 1e-06,
        ...)

Arguments

object

An object of class "Renouv" typically created by using the Renouv function.

newdata

The return period at which return levels and confidence bounds are wanted.

cov.rate

If FALSE, the delta method will not take into account the uncertainty on the event rate lambda of the Poisson process. Note however that when distname.y is "exponential" and when no MAX or OTS data is used, the value of cov.rate has no impact for now, because the delta method is not used then.

level

Confidence levels as in other 'predict' methods (not percentages).

prob

If TRUE a prob column is found in the returned data frame. This column can be used to find which quantile was used to compute the return level.

trace

Some details are printed when trace is not zero.

eps

Level of perturbation used to compute the numerical derivatives in the delta method.

...

Further arguments passed to or from other methods.

Details

Unless in some very special cases, the confidence limits are approximated ones computed by using the delta method with numerical derivatives.

Value

A data frame with the expected return levels (col. named

"quant") at the given return periods, and confidence limits. The returned object has an infer.method attribute describing the method used to compute the confidence limits.

References

Coles S. (2001) Introduction to Statistical Modelling of Extremes Values, Springer.

Author

Yves Deville

Note

Despite of its name, this method does not compute true predictions. A return period is to be interpreted as an average interevent time rather than the duration of a specific period of time. For instance, the expected return level for a given return period with length 100 years is the level that would be on average exceeded once every 100 years (assuming that the model description in object is correct).

Examples

## Use Brest data
fit <- Renouv(Brest)
#> Special inference for the exponential case without history
#> Warning: uncertainty on the rate not taken into account yet  in the exponential with no history case

pred <- predict(fit, newdata = c(100, 125, 150, 175, 200),
                level = c(0.99, 0.95))
#> Special inference for the exponential case without history
#> Warning: uncertainty on the rate not taken into account yet  in the exponential with no history case