plot_maps: Plot standard maps
In James-Thorson/VAST: Vector-Autoregressive Spatio-Temporal (VAST) Model

plot_maps

R Documentation

Plot standard maps

Description

plot_maps plots a standard set of diagnostic maps

Usage

plot_maps(
  plot_set = 3,
  fit,
  PlotDF,
  projargs = "+proj=longlat",
  Panel = "Category",
  year_labels = NULL,
  years_to_plot = NULL,
  category_names = NULL,
  quiet = FALSE,
  working_dir = getwd(),
  MapSizeRatio = NULL,
  n_cells,
  plot_value = "estimate",
  n_samples = 100,
  country = NULL,
  sample_fixed = TRUE,
  Report = fit$Report,
  TmbData = fit$data_list,
  Obj = fit$tmb_list$Obj,
  extrapolation_list = fit$extrapolation_list,
  Sdreport = fit$parameter_estimates$SD,
  Map = fit$tmb_list$Map,
  zlim = NULL,
  ...
)

Arguments

`plot_set`	integer-vector defining plots to create `plot_set=1` Link-transformed 1st linear predictor, `Report$R1_gct` `plot_set=2` Link-transformed 2nd linear predictor, logged for interpretable plotting scale, `log(Report$R2_gct)` `plot_set=3` Log-predicted density, derived from link-transform of both linear predictors, `log(Report$D_gct)` `plot_set=6` Spatio-temporal variation in 1st linear predictor (e.g., encounter probability when using a conventional delta-model), `Report$Epsilon1_gct` `plot_set=7` Spatio-temporal variation in 2nd linear predictor (e.g., log-positive catch rates when using a conventional delta-model), `Report$Epsilon2_gct` `plot_set=8` 1st linear predictor, `Report$P1_gct` `plot_set=9` 2nd linear predictor, `Report$P2_gct` `plot_set=11` Covariates that are included in the model for the 1st linear predictor, `Report$X1_gcp` `plot_set=12` Covariates that are included in the model for the 2nd linear predictor, `Report$X2_gcp` `plot_set=13` Total biomass across all categories (only useful in a multivariate model) `plot_set=14` Covariate effects on 1st linear predictor, `Report$eta1_gct` `plot_set=15` Covariate effects on 2nd linear predictor, `Report$eta2_gct` `plot_set=16` Spatial variation for 1st linear predictor, `Report$Omega1_gc` `plot_set=17` Spatial variation for 2nd linear predictor, `Report$Omega2_gc` `plot_set=18` Spatially-varying response for density covariates in 1st linear predictor, `Report$Xi1_gcp` `plot_set=19` Spatially-varying response for density covariates in 2nd linear predictor, `Report$Xi2_gcp` `plot_set=20` Spatially-varying response for catchability covariates in 1st linear predictor, `Report$Phi1_gk` `plot_set=21` Spatially-varying response for catchability covariates in 2nd linear predictor, `Report$Phi2_gk`
`projargs`	Character passed to `CRS` and see that documentation for description. See https://proj.org/operations/projections/index.html for a list of projections to pass via `projargs`. As a stable default please consider `projargs='+proj=natearth +lon_0=0 +units=km'` where argument `+lon_0` allows the user to center eastings on a specified longitude.
`Panel`	Whether to plot years for a given category (`Panel="Category"`) or categories for a given year (`Panel="Year"`) in each panel figure
`year_labels`	character vector specifying names for labeling times `t_i`
`years_to_plot`	integer vector, specifying positions of `year_labels` for plotting (used to avoid plotting years with no data, etc.)
`category_names`	character vector specifying names for labeling categories `c_i`
`MapSizeRatio`	Default size for each panel
`n_cells`	Integer used to determine the argument `cell.size` passed to `rasterize` when converting output at extrapolation-grid cells to a raster prior to plotting mapped outputs using `plot_variable`. An increased `n_cells` results in a decreased `cell.size` and hence higher resolution plots. The default value for `n_cells` will often need to be modified for a given purpose.
`plot_value`	either `plot_value="estimate"` (the default), or a user-specified function that is applied to `n_samples` samples from the joint predictive distribution, e.g., to visualize the standard error of a variable by specifying `plot_value=sd`
`n_samples`	number of samples from the joint predictive distribution for fixed and random effects. Default is 100, which is slow.
`country`	optional list of countries to display, e.g. `country = c("united states of america", "canada")`. If maps are generating visual artefacts, please try using argument `country` to simplify the polygons used to represent land features.
`sample_fixed`	whether to sample fixed and random effects, `sample_fixed=TRUE` as by default, or just sample random effects, `sample_fixed=FALSE`
`Report`	tagged list of outputs from TMB model via `Obj$report()`
`Obj`	Fitted TMB object from package 'VAST', i.e., output from '`fit_model(...)$tmb_list$Obj`'
`Sdreport`	Standard deviation outputs from TMB model via `sdreport(Obj)`
`zlim`	two numeric values, specifying range for defining bounds of color scale. If `zlim=NULL`, then a constant scale is inferred from the range of the plotted variable and a color-legend is plotted in the last panel. If `zlim=NA` then a different range is used in each panel from the range of `Y_gt[,t]` and a color-legend is plotted in every panel.
`...`	arguments passed to `plot_variable`