gpdSeqTests: GPD Multiple Threshold Goodness-of-Fit Testing
In eva: Extreme Value Analysis with Goodness-of-Fit Testing

Description Usage Arguments Details Value Examples

Wrapper function to test multiple thresholds for goodness-of-fit to the Generalized Pareto model. Can choose which test to run from the available tests in this package.

gpdSeqTests(
  data,
  thresholds = NULL,
  nextremes = NULL,
  method = c("ad", "cvm", "pbscore", "multscore", "imasym", "impb"),
  nsim = NULL,
  inner = NULL,
  outer = NULL,
  information = c("expected", "observed"),
  allowParallel = FALSE,
  numCores = 1
)

`data`	Original, full dataset in vector form.
`thresholds`	A set of threshold values (either this or a set of the number of extremes must be given, but not both). Must be provided as a vector.
`nextremes`	A set of the number of upper extremes to be used, provided as a vector.
`method`	Which test to run to sequentially test the thresholds. Must be one of ‘ad’, ‘cvm’, ‘pbscore’, ‘multscore’, ‘imasym’, or ‘impb’.
`nsim`	Number of boostrap replicates for the ‘ad’, ‘cvm’, ‘pbscore’, ‘multscore’, and ‘imasym’ tests.
`inner`	Number of inner boostrap replicates if ‘impb’ test is chosen.
`outer`	Number of outer boostrap replicates if ‘impb’ test is chosen.
`information`	To use observed or expected (default) information for the ‘pbscore’ and ‘multscore’ tests.
`allowParallel`	If selected, should the ‘cvm’, ‘ad’, ‘pbscore’, or ‘impb’ procedure be run in parallel or not. Defaults to false.
`numCores`	If allowParallel is true, specify the number of cores to use.

Function returns a matrix containing the thresholds used, the number of observations above each threshold, the corresponding test statistics, p-values (raw and transformed), and parameter estimates at each threshold. The user must provide the data, a vector of thresholds or number of upper extremes to be used, and select the test.

`threshold`	The threshold used for the test.
`num.above`	The number of observations above the given threshold.
`p.values`	Raw p-values for the thresholds tested.
`ForwardStop`	Transformed p-values according to the ForwardStop stopping rule.
`StrongStop`	Transformed p-values according to the StrongStop stopping rule.
`statistic`	Returned test statistics of each individual test.
`est.scale`	Estimated scale parameter for the given threshold.
`est.shape`	Estimated shape parameter for the given threshold.

set.seed(7)
x <- rgpd(10000, loc = 0, scale = 5, shape = 0.2)
## A vector of thresholds to test
threshes <- c(1.5, 2.5, 3.5, 4.5, 5.5)
gpdSeqTests(x, thresholds = threshes, method = "ad")

  testnum threshold num.above  p.values ForwardStop StrongStop statistic
1       1       1.5      7487 0.2725720   0.8337505  0.7710744 0.5244226
2       2       2.5      6253 0.8300439   0.9626280  0.9199867 0.2464315
3       3       3.5      5224 0.5783037   0.6927657  1.0219718 0.3485234
4       4       4.5      4371 0.3657002   0.6074136  0.9270279 0.4569545
5       5       5.5      3714 0.5321435   0.7595937  0.9866237 0.3689956
  est.scale est.shape
1  5.254336 0.2139366
2  5.382598 0.2221351
3  5.568994 0.2252624
4  5.841078 0.2208929
5  5.964935 0.2294259