Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for multiple categories (species, size, or age classes) when standardizing survey or fishery-dependent data.
Builds upon a previous R package
SpatialDeltaGLMM (public available here), and has unit-testing to automatically confirm that
SpatialDeltaGLMM give identical results (to the 3rd decimal place for parameter estimates) for several varied real-world case-study examples
Has built in diagnostic functions and model-comparison tools
Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods
Background This tool is designed to estimate spatial variation in density using spatially referenced data, with the goal of habitat associations (correlations among species and with habitat) and estimating total abundance for a target species in one or more years. The model builds upon spatio-temporal delta-generalized linear mixed modelling techniques (Thorson Shelton Ward Skaug 2015 ICESJMS), which separately models the proportion of tows that catch at least one individual ("encounter probability") and catch rates for tows with at least one individual ("positive catch rates"). Submodels for encounter probability and positive catch rates by default incorporate variation in density among years (as a fixed effect), and can incorporate variation among sampling vessels (as a random effect, Thorson and Ward 2014) which may be correlated among categories (Thorson Fonner Haltuch Ono Winker In press). Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug Kristensen Shelton Ward Harms Banante 2014 Ecology), which imply that correlations in spatial variation decay as a function of distance.
There are eight main resources for learning about VAST:
?VAST::Data_Fnin the R-terminal after installing the package.
If there are questions that arise after this, please look for a VAST Point-of-Contact at your institution and consider contacting them prior to posting an issue.
This function depends on R version >=3.1.1 and a variety of other tools.
First, install the "devtools" package from CRAN
# Install and load devtools package install.packages("devtools") library("devtools")
Note: at the moment, TMB and INLA can be installed using the commands
# devtools command to get TMB from GitHub install_github("kaskr/adcomp/TMB") # source script to get INLA from the web source("http://www.math.ntnu.no/inla/givemeINLA.R")
Next, please install the VAST package from this GitHub repository using a function in the "devtools" package:
# Install package install_github("james-thorson/VAST") # Load package library(VAST)
Thorson, In press. Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea. Fish Fish.
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