## S3 method for class 'evmOpt':
predict(object, M = 1000, newdata = NULL,
type = "return level", se.fit = FALSE,
ci.fit = FALSE, alpha = 0.05, unique. = TRUE,...)## S3 method for class 'evmSim':
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 'evmBoot':
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 'evmOpt':
linearPredictors(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE,
alpha = 0.05, unique. = TRUE, full.cov = FALSE,...)
## S3 method for class 'evmSim':
linearPredictors(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE,
alpha = 0.050, unique. = TRUE, all = FALSE, sumfun = NULL,...)
## S3 method for class 'evmBoot':
linearPredictors(object, newdata = NULL, se.fit = FALSE,
ci.fit = FALSE, alpha = 0.050, unique. = TRUE,
all = FALSE, sumfun = NULL,...)
## S3 method for class 'lp.evmOpt':
print(x, digits=3,...)
## S3 method for class 'lp.evmSim':
print(x, digits=3,...)
## S3 method for class 'lp.evmBoot':
print(x, digits=3,...)
## S3 method for class 'lp.evmOpt':
summary(object, digits=3,...)
## S3 method for class 'lp.evmSim':
summary(object, digits=3,...)
## S3 method for class 'lp.evmBoot':
summary(object, digits=3,...)
## S3 method for class 'lp.evmOpt':
plot(x, main=NULL, pch=1, ptcol=2, cex=.75,
linecol=4, cicol=1, polycol=15,...)
## S3 method for class 'lp.evmSim':
plot(x, type="median", ...)
## S3 method for class 'lp.evmBoot':
plot(x, type="median", ...)
evmOpt, evmSim or evmBoot.newdata = NULL in which case the data used in fitting the model
will be used. Column names must match those of the original data
matrix used for model fitting.type = "return level". When a return level is wanted, the
user can specify the associated return period via thse.fit = FALSE and is not implemented for
predict.evmSim or predict.evmBoot.ci.fit = FALSE. For objects of class
evmOpt, if set to TRUE
then the confidence interval is a simple symmetric confidence interval
bM = 1000. If a vector is passed, a list is returned, with items
corresponding to the different values of the vector M.ci.fit = TRUE, a 100(1 - alpha)% confidence interval is returned.
Defaults to alpha = 0.050.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.evmSim and evmBoot 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 alist
object. This is used internally and not intended for direct use.
Defaults to full.cov = FALSEevmSim and evmBoot 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.lp.evmOpt, lp.evmSim
or lp.evmBoot, to be passed to methods for these classes.M.evmBoot method,
estimates of confidence intervals are simply quantiles of the bootstrap
sample. The evmBoot
method is just a wrapper for the evmSim method.