Predict return levels from extreme value models, or obtain the linear predictors.
# S3 method for lp.evmOpt
plot(
x,
main = NULL,
pch = 1,
ptcol = 2,
cex = 0.75,
linecol = 4,
cicol = 1,
polycol = 15,
plot. = TRUE,
...
)# S3 method for evmOpt
predict(
object,
M = 1000,
newdata = NULL,
type = "return level",
se.fit = FALSE,
ci.fit = FALSE,
alpha = 0.05,
unique. = TRUE,
...
)
# S3 method for evmOpt
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 = 0.05,
unique. = TRUE,
...
)
# S3 method for evmSim
predict(
object,
M = 1000,
newdata = NULL,
type = "return level",
se.fit = FALSE,
ci.fit = FALSE,
alpha = 0.05,
unique. = TRUE,
all = FALSE,
sumfun = NULL,
...
)
# S3 method for evmSim
linearPredictors(
object,
newdata = NULL,
se.fit = FALSE,
ci.fit = FALSE,
alpha = 0.05,
unique. = TRUE,
all = FALSE,
sumfun = NULL,
...
)
# S3 method for evmBoot
predict(
object,
M = 1000,
newdata = NULL,
type = "return level",
se.fit = FALSE,
ci.fit = FALSE,
alpha = 0.05,
unique. = TRUE,
all = FALSE,
sumfun = NULL,
...
)
# S3 method for evmBoot
linearPredictors(
object,
newdata = NULL,
se.fit = FALSE,
ci.fit = FALSE,
alpha = 0.05,
unique. = TRUE,
all = FALSE,
sumfun = NULL,
...
)
# S3 method for lp.evmOpt
print(x, digits = 3, ...)
A list with two entries: the first being the call and the
second being a further list with one entry for each value of
M
.
An object of class lp.evmOpt
, lp.evmSim
or
lp.evmBoot
, to be passed to methods for these classes.
Further arguments to plot methods.
Further arguments to methods.
An object of class evmOpt
, evmSim
or
evmBoot
.
The return period: 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
.
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 the original data
matrix used for model fitting.
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 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.
Whether or not to return the standard error of the predicted
value. Defaults to se.fit = FALSE
and is not implemented for
predict.evmSim
or predict.evmBoot
.
Whether or not to return a confidence interval for the
predicted value. Defaults to 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.
If ci.fit = TRUE
, a 100(1 - alpha)% confidence interval
is returned. Defaults to alpha = 0.050
.
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
.
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
For the 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
.
For the 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
.
Number of digits to show when printing objects.
Harry Southworth and Janet E. Heffernan
By default, return levels predicted from the unique values of the linear
predictors are returned. For the evmBoot
method, estimates of
confidence intervals are simply quantiles of the bootstrap sample. The
evmBoot
method is just a wrapper for the evmSim
method.