Description Usage Arguments Details Value Methods (by class) Examples
Evaluate uncertainty in the distribution of the maximum value over some user defined length of time assuming the underlying data generating mechanism is the 2D extremal Poisson process with the Gumbel like intensity function (i.e. the tail length is zero)
1 2 3 4 5 6 7 8 9 | gumbelMaxDistUncert(x, lt_gen, n_mc, n_boot, progress_tf = TRUE, ...)
## S3 method for class 'gumbel_pot_fit'
gumbelMaxDistUncert(x, lt_gen, n_mc, n_boot,
progress_tf = TRUE)
## Default S3 method:
gumbelMaxDistUncert(x, cov_mat, thresh, lt_gen, n_mc,
n_boot, progress_tf = TRUE)
|
x |
An S3 object of class |
lt_gen |
Length of each generated series. The units (seconds, minutes,
hours, etc.) should be consistent with the value of |
n_mc |
The number of samples to draw from the distribution of the maximum |
n_boot |
The number of bootstrap replicates of the distribution of the maximum to create. |
progress_tf |
Display a progress bar if TRUE, else not. |
cov_mat |
The covariance matrix to use to perturn the MLE (most usually the negative inverse of the Hessian matrix) |
thresh |
The threshold |
The results of fitting a Gumbel like 2D extremal Poisson process are
fed into this function. The Hessian matrix is used to repeatedly perturb
the MLE, and for each set of perturbed parameters the distribution of the
maximum is empirically constructed as described in gumbelMaxDist
.
The bootstrap replicates of the distribution of the maximum may be used to
quantify uncertainty and construct intervals.
An S3 object of class gumbel_max_dist_uncert
with elements
$par
The parameters used to generate the random processes
$cov_mat
The covariance matrix (negative inverse Hessian)
used to perturb $par
$thres
The threshold used
$lt_gen
The value of the lt_gen
argument
$boot_samps
A matrix n_boot
rows and n_mc
columns containing the bootstrap replicates of the distribution of the
maximum
gumbel_pot_fit
:
default
:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Not run:
complete_series <- -jp1tap1715wind270$value
declustered_obs <- decluster(complete_series)
thresholded_obs <- gumbelEstThreshold(x = declustered_obs,
lt = 100,
n_min = 10,
n_max = 100)
gumbel_pot_fit <- gumbelMLE(x = thresholded_obs,
hessian_tf = TRUE)
gumbel_max_dist_uncert <- gumbelMaxDistUncert(x = gumbel_pot_fit, lt_gen = 200,
n_mc = 1000, n_boot = 200)
## End(Not run)
|
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