inst/scripts/93.biomass_index_carstm_2022_comparisons.r
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
source( file.path( code_root, "bio_startup.R" ) )
require(bio.snowcrab) # loadfunctions("bio.snowcrab")
year.assessment = 2022
yrs = 1999:year.assessment
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=2526 )
snowcrab_filter_class = "fb" # fishable biomass (including soft-shelled ) "m.mat" "f.mat" "imm"
loadfunctions("bio.snowcrab")
# key name
carstm_model_label= paste( "default", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# variation 1 is about removing spe comp and temp
carstm_model_label= paste( "1999_2022_variation1", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla.reduced1"
# removing stations in 2015
carstm_model_label= paste( "1999_2022_variation2", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# variation 3 is about removing spatial and spatiotemporal effects
carstm_model_label= paste( "1999_2022_variation3", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla.reduced3"
# params for number
pN = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "nbinomial",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "gaussian",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# use what was defined in the main script
sppoly=areal_units( p=pN )
additional_features = snowcrab_mapping_features(pN) # for mapping below
tmap_mode("plot")
# -------------------------------------------------
# Part 1 -- construct basic parameter list defining the main characteristics of the study
# require(aegis)
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly ) # will redo if not found
# can just copy datafile: carstm_inputs_snowcrab~tesselation~1~snowcrab~24~1~none~snowcrab_managementareas.rdata
# into working directory to speed things up
# extract results
p = pN # bio.snowcrab::snowcrab_parameters( project_class="carstm", yrs=1999:year.assessment )
p = pW
p = pH
io = which(M$tag=="observations")
ip = which(M$tag=="predictions")
iq = unique( c( which( M$totno > 0), ip ) ) # for totno
iw = unique( c( which( M$totno > 5), ip ) ) # need a good sample to estimate mean size
iqo = intersect(iq, io)
M$density = M$totno / M$data_offset
if (p==pN) Mfit = M[iq, ]
if (p==pW) Mfit = M[iw, ]
if (p==pH) Mfit = M
iobs = which(Mfit$tag=="observations")
fit = carstm_model( p=p, DS="modelled_fit", sppoly = sppoly ) # extract currently saved model fit
obs = Mfit$density[iobs]
obs = Mfit$meansize[iobs]
obs = Mfit$pa[iobs]
preds = fit$summary.fitted.values[["mean"]][iobs]
plot( log(preds) ~ log(obs) )
# plot( (preds) ~ jitter(obs) )
outp =c(
pearson=cor( preds, obs ),
spearman=cor(preds, obs, method = "spearman" ),
dic=fit$dic$dic,
p.eff=fit$dic$p.eff,
waic=fit$waic$waic,
waic.p.eff=fit$waic$p.eff,
mlk=fit$mlik[["log marginal-likelihood (Gaussian)",1]],
n = round(length(iobs),0)
)
# # plot(fit)
# plot( fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
# names(fit$marginals.hyperpar)
# plot( fit$marginals.hyperpar$"Phi for space_time", type="l") # posterior distribution of phi nonspatial dominates
# plot( fit$marginals.hyperpar$"Precision for space_time", type="l")
# plot( fit$marginals.hyperpar$"size for the nbinomial observations (1/overdispersion)", type="l")
# # fit = NULL
res = carstm_model( p=p, DS="carstm_modelled_summary", sppoly = sppoly ) # to load currently saved results
options(width =140)
options(scipen =4, digits=4)
res$summary$fixed_effects$cv = round( res$summary$fixed_effects$sd / res$summary$fixed_effects$mean * 100, 2 )
res$summary$random_effects$cv = round( res$summary$random_effects$sd / res$summary$random_effects$mean * 100, 2 )
outeff = rbind(
round( res$summary$fixed_effects[, c("mean", "sd", "cv") ], 5),
round( res$summary$random_effects[, c("mean", "sd", "cv")] , 5 )
)
rownames(outeff) = gsub( ', method = \"quantile\", n = 11)', '', rownames(outeff), fixed=TRUE)
rownames(outeff) = gsub( 'inla.group(', '', rownames(outeff), fixed=TRUE)
rownames(outeff) = gsub( 'size for the nbinomial observations (1/overdispersion)', 'nbin 1/overdispersion', rownames(outeff), fixed=TRUE)
out = list( params=outeff, gof=round(outp,3) )
out
carstm_model_label= paste( "default", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# params for number
pN0 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "nbinomial",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW0 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "gaussian",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH0 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# removing stations in 2015
carstm_model_label= paste( "1999_2022_variation2", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# params for number
pN2 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "nbinomial",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW2 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "gaussian",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH2 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
SM0 = aggregate_simulations(
fn=carstm_filenames( pN0, returnvalue="filename", fn="aggregated_timeseries" ),
redo=FALSE
)$cfasouth
SM0 = SM0[ which(rownames(SM0)=="2015"),]
SM0 = data.frame( B=SM0, Scenario="0")
SM2 = aggregate_simulations(
fn=carstm_filenames( pN2, returnvalue="filename", fn="aggregated_timeseries" ),
redo=FALSE
)$cfasouth
SM2 = SM2[ which(rownames(SM2)=="2015"),]
SM2= data.frame( B=SM2, Scenario="1") # called scenario 1 in doc
out = rbind(SM0, SM2)
ggplot(out, aes(x = B, fill = Scenario)) + # Draw overlaying histogram
geom_histogram(position = "identity", alpha = 0.52, bins = 50)
## specific to are in question:
fnout = file.path( pN2$modeldir, pN2$carstm_model_label, "unsampled_polygon.dat" )
X = read.csv (fnout) # outline of unsampled region
X$X= NULL
X = as.matrix( rbind(X, X[1,]))
X = st_sfc( st_multipoint( X ) )
X = st_cast(X, "POLYGON" )
X = st_simplify(X)
X = st_make_valid(X)
st_crs(X) = crs_lonlat
X = st_transform( X, st_crs( sppoly ) )
X = st_cast( st_as_sf(X), "MULTIPOLYGON" )
ooo = st_intersection( X, sppoly )
ooo$surfacearea = st_area( ooo )
sppoly[[ "aoi_sa_km2" ]] = 0
j = match( ooo$AUID, sppoly$AUID )
if (length(j) > 0) sppoly[[ "aoi_sa_km2" ]][j] = ooo$surfacearea
sims0 = carstm_posterior_simulations( pN=pN0, pW=pW0, pH=pH0, pa_threshold=0.05, qmax=0.99 )
sims0 = sims0 / 10^6 # 10^6 kg -> kt;; kt/km^2
SM0_aoi = colSums( sims0 * sppoly$aoi_sa_km2, na.rm=TRUE )
SM0_aoi = SM0_aoi[ which(rownames(SM0_aoi)=="2015"),]
sims2 = carstm_posterior_simulations( pN=pN2, pW=pW2, pH=pH2, pa_threshold=0.05, qmax=0.99 )
sims2 = sims2 / 10^6 # 10^6 kg -> kt;; kt/km^2
SM2_aoi = colSums( sims2 * sppoly$aoi_sa_km2, na.rm=TRUE )
SM2_aoi = SM2_aoi[ which(rownames(SM2_aoi)=="2015"),]
SM0= data.frame( B=SM0_aoi, Scenario="0") # called scenario 1 in doc
SM2= data.frame( B=SM2_aoi, Scenario="1") # called scenario 1 in doc
out = rbind(SM0, SM2)
ggplot(out, aes(x = B, fill = Scenario)) + # Draw overlaying histogram
geom_histogram(position = "identity", alpha = 0.52, bins = 50)
outputdir = file.path( p$modeldir, p$carstm_model_label, "residuals" )
# extract results and examine
# prediction surface
crs_lonlat = st_crs(projection_proj4string("lonlat_wgs84"))
sppoly = areal_units( p=p ) # will redo if not found
sppoly = st_transform(sppoly, crs=crs_lonlat )
# do this immediately to reduce storage for sppoly (before adding other variables)
S = snowcrab.db( p=p, DS="biological_data" ) # will redo if not found .. not used here but used for data matching/lookup in other aegis projects that use bathymetry
S$tiyr=lubridate::decimal_date(S$timestamp)
# S$totno = S$totno_adjusted / S$cf_set_no # convert density to counts
# S$totwgt = S$totwgt_adjusted / S$cf_set_mass # convert density to total wgt
# S$data_offset = 1 / S$cf_set_no ## offset only used in poisson model
# reduce size
S = S[ which( S$lon > p$corners$lon[1] & S$lon < p$corners$lon[2] & S$lat > p$corners$lat[1] & S$lat < p$corners$lat[2] ), ]
# levelplot(z.mean~plon+plat, data=S, aspect="iso")
S$AUID = st_points_in_polygons(
pts = st_as_sf( S, coords=c("lon","lat"), crs=crs_lonlat ),
polys = sppoly[, "AUID"],
varname="AUID"
)
S = S[!is.na(S$AUID),]
names(S)[which(names(S)=="yr") ] = "year"
# S = S[ which(S$year %in% p$yrs), ]
# S$tiyr = lubridate::decimal_date ( S$timestamp )
# S$dyear = S$tiyr - S$year
obsMM = M[M$tag=="observations",]
plocs = M[M$tag=="predictions",]
obs = S
obs$density = obs$totwgt / obs$data_offset
rr = as.data.table(sims)
rr$AUID = as.character( rr$space)
# rr$AUID = match( rr$AUID, sppoly$AUID)
rr$year = as.numeric( as.character( rr$time) )
st = rr[,.(mean=mean(value)), by=.(AUID, year) ]
obs = merge( obs, st, by=c("year", "AUID"), all.x=TRUE, all.y=FALSE )
obs$mean[ !is.finite(obs$mean) ] = 0
obs$resid = obs$mean - obs$density
obs$resid_per_set = obs$resid * obs$data_offset
obs$yr = obs$year
plot(mean~density, obs)
vn = "resid"
vn = "resid_per_set"
#er = range( obs[,vn], na.rm=T) * c(0.95, 1.05)
er = c(-100, 100)
resol = p$pres
B = bathymetry_db(p=p, DS="baseline") # 1 km (p$pres )
for ( y in 2000:2018 ) {
ii = which( obs$yr==y & is.finite(obs[,vn] ))
if ( length(ii) > 3 ) {
dir.create( file.path( outputdir, "residuals", p$carstm_model_label), recursive=TRUE, showWarnings =FALSE)
fn = file.path( outputdir, "residuals", p$carstm_model_label, paste( "residuals", y, "png", sep=".") )
png( filename=fn, width=3072, height=2304, pointsize=40, res=300 )
lp = map_simple( toplot=obs[ ii, c("plon","plat", vn) ], plotarea=B, resol=1, theta=15, filterdistances=7.5, vn=vn, annot=paste("Residuals", y), er=er )
print(lp)
dev.off()
print(fn)
}
}
jae0/snowcrab documentation built on Nov. 6, 2024, 10:13 p.m.
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source( file.path( code_root, "bio_startup.R" ) )
require(bio.snowcrab) # loadfunctions("bio.snowcrab")
year.assessment = 2022
yrs = 1999:year.assessment
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=2526 )
snowcrab_filter_class = "fb" # fishable biomass (including soft-shelled ) "m.mat" "f.mat" "imm"
loadfunctions("bio.snowcrab")
# key name
carstm_model_label= paste( "default", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# variation 1 is about removing spe comp and temp
carstm_model_label= paste( "1999_2022_variation1", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla.reduced1"
# removing stations in 2015
carstm_model_label= paste( "1999_2022_variation2", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# variation 3 is about removing spatial and spatiotemporal effects
carstm_model_label= paste( "1999_2022_variation3", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla.reduced3"
# params for number
pN = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "nbinomial",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "gaussian",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# use what was defined in the main script
sppoly=areal_units( p=pN )
additional_features = snowcrab_mapping_features(pN) # for mapping below
tmap_mode("plot")
# -------------------------------------------------
# Part 1 -- construct basic parameter list defining the main characteristics of the study
# require(aegis)
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly ) # will redo if not found
# can just copy datafile: carstm_inputs_snowcrab~tesselation~1~snowcrab~24~1~none~snowcrab_managementareas.rdata
# into working directory to speed things up
# extract results
p = pN # bio.snowcrab::snowcrab_parameters( project_class="carstm", yrs=1999:year.assessment )
p = pW
p = pH
io = which(M$tag=="observations")
ip = which(M$tag=="predictions")
iq = unique( c( which( M$totno > 0), ip ) ) # for totno
iw = unique( c( which( M$totno > 5), ip ) ) # need a good sample to estimate mean size
iqo = intersect(iq, io)
M$density = M$totno / M$data_offset
if (p==pN) Mfit = M[iq, ]
if (p==pW) Mfit = M[iw, ]
if (p==pH) Mfit = M
iobs = which(Mfit$tag=="observations")
fit = carstm_model( p=p, DS="modelled_fit", sppoly = sppoly ) # extract currently saved model fit
obs = Mfit$density[iobs]
obs = Mfit$meansize[iobs]
obs = Mfit$pa[iobs]
preds = fit$summary.fitted.values[["mean"]][iobs]
plot( log(preds) ~ log(obs) )
# plot( (preds) ~ jitter(obs) )
outp =c(
pearson=cor( preds, obs ),
spearman=cor(preds, obs, method = "spearman" ),
dic=fit$dic$dic,
p.eff=fit$dic$p.eff,
waic=fit$waic$waic,
waic.p.eff=fit$waic$p.eff,
mlk=fit$mlik[["log marginal-likelihood (Gaussian)",1]],
n = round(length(iobs),0)
)
# # plot(fit)
# plot( fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
# names(fit$marginals.hyperpar)
# plot( fit$marginals.hyperpar$"Phi for space_time", type="l") # posterior distribution of phi nonspatial dominates
# plot( fit$marginals.hyperpar$"Precision for space_time", type="l")
# plot( fit$marginals.hyperpar$"size for the nbinomial observations (1/overdispersion)", type="l")
# # fit = NULL
res = carstm_model( p=p, DS="carstm_modelled_summary", sppoly = sppoly ) # to load currently saved results
options(width =140)
options(scipen =4, digits=4)
res$summary$fixed_effects$cv = round( res$summary$fixed_effects$sd / res$summary$fixed_effects$mean * 100, 2 )
res$summary$random_effects$cv = round( res$summary$random_effects$sd / res$summary$random_effects$mean * 100, 2 )
outeff = rbind(
round( res$summary$fixed_effects[, c("mean", "sd", "cv") ], 5),
round( res$summary$random_effects[, c("mean", "sd", "cv")] , 5 )
)
rownames(outeff) = gsub( ', method = \"quantile\", n = 11)', '', rownames(outeff), fixed=TRUE)
rownames(outeff) = gsub( 'inla.group(', '', rownames(outeff), fixed=TRUE)
rownames(outeff) = gsub( 'size for the nbinomial observations (1/overdispersion)', 'nbin 1/overdispersion', rownames(outeff), fixed=TRUE)
out = list( params=outeff, gof=round(outp,3) )
out
carstm_model_label= paste( "default", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# params for number
pN0 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "nbinomial",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW0 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "gaussian",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH0 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# removing stations in 2015
carstm_model_label= paste( "1999_2022_variation2", snowcrab_filter_class, sep="_" )
carstm_modelengine = "inla"
# params for number
pN2 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "nbinomial",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW2 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "gaussian",
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH2 = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
carstm_model_label= carstm_model_label,
carstm_modelengine = carstm_modelengine,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
SM0 = aggregate_simulations(
fn=carstm_filenames( pN0, returnvalue="filename", fn="aggregated_timeseries" ),
redo=FALSE
)$cfasouth
SM0 = SM0[ which(rownames(SM0)=="2015"),]
SM0 = data.frame( B=SM0, Scenario="0")
SM2 = aggregate_simulations(
fn=carstm_filenames( pN2, returnvalue="filename", fn="aggregated_timeseries" ),
redo=FALSE
)$cfasouth
SM2 = SM2[ which(rownames(SM2)=="2015"),]
SM2= data.frame( B=SM2, Scenario="1") # called scenario 1 in doc
out = rbind(SM0, SM2)
ggplot(out, aes(x = B, fill = Scenario)) + # Draw overlaying histogram
geom_histogram(position = "identity", alpha = 0.52, bins = 50)
## specific to are in question:
fnout = file.path( pN2$modeldir, pN2$carstm_model_label, "unsampled_polygon.dat" )
X = read.csv (fnout) # outline of unsampled region
X$X= NULL
X = as.matrix( rbind(X, X[1,]))
X = st_sfc( st_multipoint( X ) )
X = st_cast(X, "POLYGON" )
X = st_simplify(X)
X = st_make_valid(X)
st_crs(X) = crs_lonlat
X = st_transform( X, st_crs( sppoly ) )
X = st_cast( st_as_sf(X), "MULTIPOLYGON" )
ooo = st_intersection( X, sppoly )
ooo$surfacearea = st_area( ooo )
sppoly[[ "aoi_sa_km2" ]] = 0
j = match( ooo$AUID, sppoly$AUID )
if (length(j) > 0) sppoly[[ "aoi_sa_km2" ]][j] = ooo$surfacearea
sims0 = carstm_posterior_simulations( pN=pN0, pW=pW0, pH=pH0, pa_threshold=0.05, qmax=0.99 )
sims0 = sims0 / 10^6 # 10^6 kg -> kt;; kt/km^2
SM0_aoi = colSums( sims0 * sppoly$aoi_sa_km2, na.rm=TRUE )
SM0_aoi = SM0_aoi[ which(rownames(SM0_aoi)=="2015"),]
sims2 = carstm_posterior_simulations( pN=pN2, pW=pW2, pH=pH2, pa_threshold=0.05, qmax=0.99 )
sims2 = sims2 / 10^6 # 10^6 kg -> kt;; kt/km^2
SM2_aoi = colSums( sims2 * sppoly$aoi_sa_km2, na.rm=TRUE )
SM2_aoi = SM2_aoi[ which(rownames(SM2_aoi)=="2015"),]
SM0= data.frame( B=SM0_aoi, Scenario="0") # called scenario 1 in doc
SM2= data.frame( B=SM2_aoi, Scenario="1") # called scenario 1 in doc
out = rbind(SM0, SM2)
ggplot(out, aes(x = B, fill = Scenario)) + # Draw overlaying histogram
geom_histogram(position = "identity", alpha = 0.52, bins = 50)
outputdir = file.path( p$modeldir, p$carstm_model_label, "residuals" )
# extract results and examine
# prediction surface
crs_lonlat = st_crs(projection_proj4string("lonlat_wgs84"))
sppoly = areal_units( p=p ) # will redo if not found
sppoly = st_transform(sppoly, crs=crs_lonlat )
# do this immediately to reduce storage for sppoly (before adding other variables)
S = snowcrab.db( p=p, DS="biological_data" ) # will redo if not found .. not used here but used for data matching/lookup in other aegis projects that use bathymetry
S$tiyr=lubridate::decimal_date(S$timestamp)
# S$totno = S$totno_adjusted / S$cf_set_no # convert density to counts
# S$totwgt = S$totwgt_adjusted / S$cf_set_mass # convert density to total wgt
# S$data_offset = 1 / S$cf_set_no ## offset only used in poisson model
# reduce size
S = S[ which( S$lon > p$corners$lon[1] & S$lon < p$corners$lon[2] & S$lat > p$corners$lat[1] & S$lat < p$corners$lat[2] ), ]
# levelplot(z.mean~plon+plat, data=S, aspect="iso")
S$AUID = st_points_in_polygons(
pts = st_as_sf( S, coords=c("lon","lat"), crs=crs_lonlat ),
polys = sppoly[, "AUID"],
varname="AUID"
)
S = S[!is.na(S$AUID),]
names(S)[which(names(S)=="yr") ] = "year"
# S = S[ which(S$year %in% p$yrs), ]
# S$tiyr = lubridate::decimal_date ( S$timestamp )
# S$dyear = S$tiyr - S$year
obsMM = M[M$tag=="observations",]
plocs = M[M$tag=="predictions",]
obs = S
obs$density = obs$totwgt / obs$data_offset
rr = as.data.table(sims)
rr$AUID = as.character( rr$space)
# rr$AUID = match( rr$AUID, sppoly$AUID)
rr$year = as.numeric( as.character( rr$time) )
st = rr[,.(mean=mean(value)), by=.(AUID, year) ]
obs = merge( obs, st, by=c("year", "AUID"), all.x=TRUE, all.y=FALSE )
obs$mean[ !is.finite(obs$mean) ] = 0
obs$resid = obs$mean - obs$density
obs$resid_per_set = obs$resid * obs$data_offset
obs$yr = obs$year
plot(mean~density, obs)
vn = "resid"
vn = "resid_per_set"
#er = range( obs[,vn], na.rm=T) * c(0.95, 1.05)
er = c(-100, 100)
resol = p$pres
B = bathymetry_db(p=p, DS="baseline") # 1 km (p$pres )
for ( y in 2000:2018 ) {
ii = which( obs$yr==y & is.finite(obs[,vn] ))
if ( length(ii) > 3 ) {
dir.create( file.path( outputdir, "residuals", p$carstm_model_label), recursive=TRUE, showWarnings =FALSE)
fn = file.path( outputdir, "residuals", p$carstm_model_label, paste( "residuals", y, "png", sep=".") )
png( filename=fn, width=3072, height=2304, pointsize=40, res=300 )
lp = map_simple( toplot=obs[ ii, c("plon","plat", vn) ], plotarea=B, resol=1, theta=15, filterdistances=7.5, vn=vn, annot=paste("Residuals", y), er=er )
print(lp)
dev.off()
print(fn)
}
}
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