predict.gpd: Predict return levels from Generalized Pareto Distribution...
In texmex: Threshold exceedences and multivariate extremes

Description Usage Arguments Details Value Note Author(s)

Predict return levels from Generalized Pareto Distribution models, or obtain the linear predictors.

## S3 method for class 'gpd'
predict(object, M = 1000, newdata = NULL, type = "return level", se.fit = FALSE, 
ci.fit = FALSE, alpha = 0.05, unique. = TRUE,...)

## S3 method for class 'bgpd'
predict(object, M = 1000, newdata = NULL, type = "return level", se.fit = FALSE, 
ci.fit = FALSE, alpha = 0.050, unique. = TRUE, all = FALSE, sumfun = NULL,...)

## S3 method for class 'bootgpd'
predict(object, M = 1000, newdata = NULL, type = "return level", se.fit = FALSE, 
ci.fit = FALSE, alpha = 0.050, unique. = TRUE, all = FALSE, sumfun = NULL,...)


linearPredictors(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE, alpha = 0.050, 
unique. = TRUE, ...)

## S3 method for class 'gpd'
linearPredictors(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE, 
alpha = 0.05, unique. = TRUE, full.cov = FALSE,...)
## S3 method for class 'bgpd'
linearPredictors(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE, 
alpha = 0.050, unique. = TRUE, all = FALSE, sumfun = NULL,...)
## S3 method for class 'bootgpd'
linearPredictors(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE, alpha = 0.050, unique. = TRUE, all = FALSE, sumfun = NULL,...)

## S3 method for class 'lp.gpd'
print(x, digits=3,...)
## S3 method for class 'lp.bgpd'
print(x, digits=3,...)
## S3 method for class 'lp.bootgpd'
print(x, digits=3,...)

## S3 method for class 'lp.gpd'
summary(object, digits=3,...)
## S3 method for class 'lp.bgpd'
summary(object, digits=3,...)
## S3 method for class 'lp.bootgpd'
summary(object, digits=3,...)

## S3 method for class 'lp.gpd'
plot(x, main=NULL, pch=1, ptcol=2, cex=.75, linecol=4, cicol=1, polycol=15,...)
## S3 method for class 'lp.bgpd'
plot(x, type="median", ...)
## S3 method for class 'lp.bootgpd'
plot(x, type="median", ...)

`object`	An object of class `gpd`, `bgpd` or `bootgpd`.
`newdata`	The new data that you want to make the prediction for. Defaults in `newdata = NULL` in which case the data used in fitting the model will be used. Column names must match those of original data matrix used for model fitting.
`type`	For the predict methods, the type of prediction, either “return level” (or “rl”) or “link” (or “lp”). Defaults to `type = "return level"`. When a return level is wanted, the user can specify the associated return perdiod via the `M` argument. If type = ``link'' the linear predictor(s) for `phi` and `xi` are returned. For the plot methods for simulation based estimation of underlying distributions i.e. objects derived from bgpd and bootgpd classes, whether to use the sample median `type="median"` or mean `type="mean"` estimate of the parameter.
`se.fit`	Whether or not to return the standard error of the predicted value. Defaults to `se.fit = FALSE` and is not implemented for `predict.bgpd` or `predict.bootgpd`.
`ci.fit`	Whether or not to return a confidence interval for the predicted value. Defaults to `ci.fit = FALSE`. For objects of class `gpd`, if set to `TRUE` then the confidence interval is a simple symmetric confidence interval based on the estimated approximate standard error. For the `bgpd` and `bootgpd` methods, the confidence interval represents quantiles of the simulated distribution of the parameters.
`M`	The return level: units are number of observations. Defaults to `M = 1000`. If a vector is passed, a list is returned, with items corresponding to the different values of the vector `M`.
`alpha`	If `ci.fit = TRUE`, a (1 - alpha)% confidence interval is returned. Defaults to `alpha = 0.050`.
`unique.`	If `unique. = TRUE`, predictions for only the unique values of the linear predictors are returned, rather than for every row of `newdata`. Defaults to `unique. = TRUE`.
`all`	For the `bgpd` and `bootgpd` methods, if `all = TRUE`, the predictions are returned for every simulated parameter vector. Otherwise, only a summary of the posterior/bootstrap distribution is returned. Defaults to `all = FALSE`.
`full.cov`	Should the full covariance matrix be returned as part of a `list` object. This is used internally and not intended for direct use. Defaults to `full.cov = FALSE`
`sumfun`	For the `bgpd` and `bootgpd` methods, a summary function can be passed in. If `sumfun = FALSE`, the default, the summary function used returns the estimated mean and median, and quantiles implied by `alpha`.
`x`	An object of class `lp.gpd`, `lp.bgpd` or `lp.bootgpd`, to be passed to methods for these classes.
`main, pch, ptcol, cex, linecol, cicol, polycol`	Further arguments to plot methods.
`digits`	Number of digits to show when printing objects.
`...`	Further arguments to methods.

By default, return levels predicted from the unique values of the linear predictors are returned. For the bootgpd method, estimates of confidence intervals are simply quantiles of the bootstrap sample. The bootgpd method is just a wrapper for the bgpd method.

A list with one entry for each value of M.

At present, the confidence intervals returned for an object of class gpd are simple confidence intervals based on assumptions of normality that are likely to be far from the truth in many cases. A better approach would be to use profile likelihood, and we intend to implement this method at a future date. Alternatively, the credible intervals returned by using Bayesian estimation and the predict method for class "bgpd" will tend to give a better representation of the asymmetry of the estimated intervals around the parameter point estimates.

Harry Southworth and Janet E. Heffernan

texmex documentation built on May 2, 2019, 4:56 p.m.