phenomix

R package for fitting distributions to run timing data via maximum likelihood

pkgdown site: https://nwfsc-cb.github.io/phenomix/

DOI

The DOI for this repository is

Installation

You can install phenomix with:

remotes::install_github("nwfsc-cb/phenomix",build_vignettes = TRUE)
#> Downloading GitHub repo nwfsc-cb/phenomix@HEAD
#> 
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#>      checking for file ‘/private/var/folders/ts/4x6hzmfx7d52vbhjqmrs_3pw0000gp/T/RtmpjBCVFK/remotes161f77702fa38/nwfsc-cb-phenomix-a46f4fc/DESCRIPTION’ ...  ✔  checking for file ‘/private/var/folders/ts/4x6hzmfx7d52vbhjqmrs_3pw0000gp/T/RtmpjBCVFK/remotes161f77702fa38/nwfsc-cb-phenomix-a46f4fc/DESCRIPTION’
#>   ─  preparing ‘phenomix’:
#>      checking DESCRIPTION meta-information ...  ✔  checking DESCRIPTION meta-information
#>   ─  cleaning src
#>   ─  installing the package to build vignettes
#>      creating vignettes ...  ✔  creating vignettes (1m 34.3s)
#>   ─  cleaning src
#>   ─  checking for LF line-endings in source and make files and shell scripts
#>   ─  checking for empty or unneeded directories
#>   ─  building ‘phenomix_1.0.4.tar.gz’
#>      
#>

Load libraries

library(phenomix)
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.3.2

Functions

The package pheomix provides a suite of curve fitting to describe data that may be generated from a process when distributions in time might be concentrated (from fisheries, this occurs with counts over time of salmon returning from the ocean to spawn or juvenile fish emigrating from streams to the ocean).

Predicted (black line) and observed counts (red dots) for hypothetical dataset. Multiple observations may exist for some days, or no observations on others.

In a given year, the curve might be described by a symmetric or asymmetric Gaussian or Student-t distribution (shown here in log-scale on the y-axis). Questions of interest might be - are the means (x-axis) shifting through time? - are the variances shifting through time? - does the model support a symmetric or asymmetric distribution?

Examples

The main functions are create_data() and fit(). See ?create_data and ?fit for additional details and examples. A vignette includes additional detail, and examples of several models as well as function arguments available https://nwfsc-cb.github.io/phenomix/.

References

For description of fisheries applications of asymmetric models:

Methot, R.D. 2000. Technical description of the stock synthesis assessment program. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-43, 46 p. link

For statistical background of asymmetric models:

Rubio, F.J. and Steel, M.F.J. 2020. The family of two-piece distributions. Significance, 17(1) 120–13. link

Wallis, K.F. 2014. The two-piece normal, binormal, or double Gaussian distribution: Its origin and rediscoveries. Statistical Science, 29(1), 106–112. link

NOAA Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

U.S. Department of Commerce | National Oceanographic and Atmospheric Administration | NOAA Fisheries

nwfsc-cb / phenomix

readme