Description Usage Arguments Details Value Examples
Empirically build the distribution of the maximum value over some user defined length of time assuming the underlying data generating mechanism is a 2D extremal Poisson process with the Gumbel like intensity function (i.e. the tail length is zero), but without specifying a single threshold.
1 2 3 | ## S3 method for class 'gumbel_multi_fit'
gumbelMaxDist(x, lt_gen, n_mc,
progress_tf = TRUE)
|
x |
An S3 object of class |
lt_gen |
(numeric scalar) Length of each generated series. The units
(seconds, minutes, hours, etc.) should be consistent with the value of
|
n_mc |
(numeric scalar) The number of samples to draw from the mixture distribution of the maximums |
progress_tf |
(logical scalar) Display a progress bar if TRUE, else not. |
The results of fitting a Gumbel like 2D extremal Poisson process for
many thresholds are fed into this function. Random processes are
repeatedly generated from each fitted model, according to the weights
described in gumbelMultiFit
, and the maximum of each random process
is recorded. The recoreded maximums represent an iid sample from a mixture
of the distributions of maximum values. Each potential threshold gives
rise to a different distribution of the maximum value. Note that the
desired length of the processes generated can be different from the length
of time over which the data used to fit the models were observed.
An S3 object of class gumbel_max_dist_multi_thresh
with elements
$mu
The estimated location parameter for each threshold
$sigma
The estimated scale parameter for each threshold
$thres
The thresholds
$lt_gen
The value of the lt_gen
argument
$n_each
The number of samples drawn from each mixture
component. The sum of $n_each
should be n_mc
$max_dist
A numeric vector of length n_mc
containing
the samples from the mixture distribution of the maximums
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
complete_series <- -jp1tap1715wind270$value
declustered_obs <- decluster(complete_series)
gumbel_multi_fit <- gumbelMultiFit(x = declustered_obs, lt = 100,
n_min = 10, n_max = 50,
weight_scale = 5)
gumbel_max_dist <- gumbelMaxDist(x = gumbel_multi_fit, lt_gen = 200, n_mc = 1000)
## End(Not run)
|
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