inst/scripts/02_substrate_carstm.R

In jae0/ecofishmod: Ecosystem-based fishery stock assessment models using conditional autoregressive models to represent spatial and temporal variabilty.

# construct basic parameter list defining the main characteristics of the study
# and some plotting parameters (bounding box, projection, bathymetry layout, coastline)
  p = aegis.carstm::substrate_carstm(
    DS = "parameters",
    project_name = "substrate",
    spatial_domain = "SSE",  # defines spatial area, currenty: "snowcrab" or "SSE"
    inputdata_spatial_discretization_planar_km = 1,  # km controls resolution of data prior to modelling to reduce data set and speed up modelling
    variabletomodel ="substrate.grainsize",
    carstm_model_label = "production",
    areal_units_fn = "default",
    areal_units_resolution_km = 25, # km dim of lattice ~ 1 hr
    areal_units_proj4string_planar_km = aegis::projection_proj4string("utm20") , # coord system to use for areal estimation and gridding for carstm
    areal_units_source = "lattice", # "stmv_fields" to use ageis fields instead of carstm fields ... note variables are not the same
    areal_units_overlay = "none"
)

# prepare data
  # sppoly = areal_units( p=p, redo=TRUE )  # this has already been done in aegis.polygons::01 polygons.R .. should nto have to redo
  sppoly = areal_units( p=p )  # this has already been done in aegis.polygons::01 polygons.R .. should nto have to redo
  plot(sppoly) # spplot( sppoly, "AUID", main="AUID", sp.layout=p$coastLayout )

  M = substrate.db( p=p, DS="aggregated_data", redo=TRUE )  # will redo if not found .. not used here but used for data matching/lookup in other aegis projects that use bathymetry
  M = substrate_carstm( p=p, DS="carstm_inputs", redo=TRUE )  # will redo if not found

# run model and obtain predictions
  fit = carstm_model( p=p, M=M )

# extract results and examine
  fit =  carstm_model( p=p, DS="carstm_modelled_fit" )  # extract currently saved model fit
  res = carstm_summary( p=p  ) # to load currently saved results


  plot(fit)
  plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
  s = summary(fit)
  s$dic$dic
  s$dic$p.eff


  # maps of some of the results
  vn = paste(p$variabletomodel, "predicted", sep=".")
  carstm_plot( p=p, res=res, vn=vn )

  vn = paste(p$variabletomodel, "random_sample_iid", sep=".")
  carstm_plot( p=p, res=res, vn=vn )

  vn = paste(p$variabletomodel, "random_auid_nonspatial", sep=".")
  carstm_plot( p=p, res=res, vn=vn )

  vn = paste(p$variabletomodel, "random_auid_spatial", sep="._carstm  carstm_plot( p=p, res=res, vn=vn )


# end