https://github.com/paulnorthrop/threshr

Threshold Selection and Uncertainty for Extreme Value Analysis
https://github.com/paulnorthrop/threshr

extreme-value-statistics extremes generalized inference pareto plot prediction threshold threshold-selection uncertainty

Last synced: 9 days ago
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Threshold Selection and Uncertainty for Extreme Value Analysis

• Host: GitHub
• URL: https://github.com/paulnorthrop/threshr
• Owner: paulnorthrop
• Created: 2017-08-02T12:11:01.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-12-02T22:39:15.000Z (about 1 year ago)
• Last Synced: 2024-04-26T23:02:06.297Z (8 months ago)
• Topics: extreme-value-statistics, extremes, generalized, inference, pareto, plot, prediction, threshold, threshold-selection, uncertainty
• Language: R
• Homepage: https://paulnorthrop.github.io/threshr/
• Size: 5.26 MB
• Stars: 7
• Watchers: 1
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.Rmd

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README

        
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output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# threshr

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## Threshold Selection and Uncertainty for Extreme Value Analysis

### What does threshr do?

The `threshr` package deals primarily with the selection of thresholds for use in extreme value models. It also performs predictive inferences about future extreme values. These inferences can either be based on a single threshold or on a weighted average of inferences from multiple thresholds.  The weighting reflects an estimated measure of the predictive performance of the threshold and can incorporate prior probabilities supplied by a user.  At the moment only the simplest case, where the data can be treated as independent identically distributed observations, is considered, as described in [Northrop et al. (2017)](https://doi.org/10.1111/rssc.12159).  Future releases will tackle more general situations.  

### A simple example

The main function in the threshr package is `ithresh`.  It uses Bayesian leave-one-out cross-validation to compare the extreme value predictive ability resulting from the use of each of a user-supplied set of thresholds.  The following code produces a threshold diagnostic plot using a dataset `gom` containing 315 storm peak significant waveheights.  We set a vector `u_vec` of thresholds; call `ithresh`, supplying the data and thresholds; and use then plot the results. In this minimal example (`ithresh` has further arguments) thresholds are judged in terms of the quality of prediction of whether the validation observation lies above the highest threshold in `u_vec` and, if it does, how much it exceeds this highest threshold.

```{r, eval = FALSE}

library(threshr)

u_vec_gom <- quantile(gom, probs = seq(0, 0.9, by = 0.05))

gom_cv <- ithresh(data = gom, u_vec = u_vec_gom)

plot(gom_cv)

```

### Installation

To get the current released version from CRAN:

```{r installation, eval = FALSE}

install.packages("threshr")

```

### Vignette

See `vignette("threshr-vignette", package = "threshr")` for an overview of the package.