"predict"(object, M = 1000, newdata = NULL,
type = "return level", se.fit = FALSE,
ci.fit = FALSE, alpha = 0.05, unique. = TRUE,...)
"predict"(object, M = 1000, newdata = NULL,
type = "return level", se.fit = FALSE,
ci.fit = FALSE, alpha = 0.050, unique. = TRUE, all = FALSE,
sumfun = NULL,...)
"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, ...)
"linearPredictors"(object, newdata = NULL, se.fit = FALSE, ci.fit = FALSE,
alpha = 0.05, unique. = TRUE, full.cov = FALSE,...)
"linearPredictors"(object, newdata = NULL, 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, all = FALSE, sumfun = NULL,...)
"print"(x, digits=3,...)
"print"(x, digits=3,...)
"print"(x, digits=3,...)
"summary"(object, digits=3,...)
"summary"(object, digits=3,...)
"summary"(object, digits=3,...)
"plot"(x, main=NULL, pch=1, ptcol=2, cex=.75, linecol=4, cicol=1, polycol=15,...)
"plot"(x, type="median", ...)
"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 the M
argument. If type = "link"
the linear predictor(s) for
phi
and xi
(or whatever other parameters are
in your texmexFamily
are returned. For the plot methods for simulation based estimation of
underlying distributions i.e. objects derived from "evmSim" and
"evmBoot" classes, whether to use the sample median
type="median"
or mean type="mean"
estimate of the
parameter.
se.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
based on the estimated approximate standard error. For the evmSim
and evmBoot
methods, the confidence
interval represents quantiles of the simulated distribution of the
parameters.
M = 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 all = FALSE
.
list
object. This is used internally and not intended for direct use.
Defaults to full.cov = FALSE
evmSim
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.