## SLOW :: about 1 hr for each config x 25 configs .. ie., 24 hrs .. consider removing configs if no need for posterior samples
# construct basic parameter list defining the main characteristics of the study
# and some plotting parameters (bounding box, projection, bathymetry layout, coastline)
p = aegis.carstm::bathymetry_carstm(
DS = "parameters",
project_name = "bathymetry",
spatial_domain = "SSE", # defines spatial area, currenty: "snowcrab" or "SSE"
variabletomodel ="z",
carstm_model_label = "production",
inputdata_spatial_discretization_planar_km = 1, # 1 km .. some thinning .. requires 32 GB RAM and limit of speed -- controls resolution of data prior to modelling to reduce data set and speed up modelling
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"
)
# example sequence to force creating of input data for modelling
sppoly = areal_units( p=p, redo=TRUE ); plot(sppoly) # or: spplot( sppoly, "AUID", main="AUID", sp.layout=p$coastLayout )
M = bathymetry.db( p=p, DS="aggregated_data" , redo=TRUE ) # will redo if not found .. not used here but used for data matching/lookup in other aegis projects that use bathymetry
M = bathymetry_carstm( p=p, DS="carstm_inputs", redo=TRUE ) # will redo if not found
str(M)
# run the model ... about 24 hrs
fit = carstm_model( p=p, M='bathymetry_carstm( p=p, DS="carstm_inputs" )' ) # run model and obtain predictions
# loading saved results
fit = carstm_model( p=p, DS="carstm_modelled_fit" ) # extract currently saved model fit
res = carstm_summary( p=p ) # to load currently saved sppoly
plot(fit)
plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
s = summary(fit)
s$dic$dic # 1404489
s$dic$p.eff # 151412
# maps of some of the results
vn = paste(p$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_plot( p=p, res=res, vn=vn )
# end
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