Ordination

Spatio-temporal ordination

It is possible to use VAST to conduct "ordination." This process estimates a reduced set of axes that collectively explain variability in a data set. By identifying species that have similar "loadings" for these different axes, ordination can identify species that are similar. As an example, please see code below. At time of writing, this requires installing the development branch of FishStatsUtils, although future releases will include this code in the master branch and I may forget to update this page.

# Download release number 3.0.0; its useful for reproducibility to use a specific release number
devtools::install_github("james-thorson-NOAA/VAST")

# Decide where to run and save results
#setwd( "D:/UW Hideaway (SyncBackFree)/AFSC/2019-04 -- Wrapper function demo for ordination" )

# Load packages
library(VAST)

# load data set
# see `?load_example` for list of stocks with example data
# that are installed automatically with `FishStatsUtils`.
example = load_example( data_set="five_species_ordination" )

# Make settings
settings = make_settings( n_x = 100, 
  Region = example$Region, 
  purpose = "ordination",
  strata.limits = example$strata.limits, 
  n_categories = 2 )

# Modify settings to allow model to run faster for demo 
settings$FieldConfig['Beta',] = "IID"
settings$FieldConfig['Epsilon',] = 0
settings$RhoConfig[] = 0

# Run model
fit = fit_model( settings = settings, 
  Lat_i = example$sampling_data[,'Lat'], 
  Lon_i = example$sampling_data[,'Lon'],
  t_i = example$sampling_data[,'Year'], 
  c_i = as.numeric(example$sampling_data[,"species_number"])-1,
  b_i = example$sampling_data[,'Catch_KG'], 
  a_i = example$sampling_data[,'AreaSwept_km2'],
  newtonsteps = 0,
  getsd = FALSE )

# Plot results
results = plot( fit )

# Plot correlations (showing Omega1 as example)
require(corrplot)
Cov_omega1 = fit$Report$L_omega1_cf %*% t(fit$Report$L_omega1_cf)
corrplot( cov2cor(Cov_omega1), method="pie", type="lower")
corrplot.mixed( cov2cor(Cov_omega1) )