inst/scripts/10_cod_basic_stranal.R
In jae0/ecofishmod: Ecosystem-based fishery stock assessment models using conditional autoregressive models to represent spatial and temporal variabilty.
# ------------------------------------------------
# Atlantic cod comparison of naive strata-based averages
# This replicates standard groundfish strata-based estimation of means and totals
# "standard" random-stratified estimation functions (based on stratanal and bootstrap estimation techniques )
# derived from numerous authors .. this code has been optimized a bit more for clarity and speed by JSC
# ------------------------------------------------
# load data environment
RES = data.frame(yr=1970:2017) # collect model comparisons in this data frame
if (0) {
fn = file.path( getwd(), "RES.rdata" )
# save(RES, file=fn)
# load(fn)
}
yrs =2000:2018
for (tu in c( "standardtow", "towdistance", "sweptarea") ) {
# construct basic parameter list defining the main characteristics of the study and some plotting params
p = carstm::carstm_parameters(
label ="Atlantic cod summer standardtow",
speciesname = "Atlantic_cod",
groundfish_species_code = 10, # 10= cod
yrs = yrs,
areal_units_timeperiod = "pre2014", # "pre2014" for older
areal_units_proj4string_planar_km = projection_proj4string("omerc_nova_scotia"), # oblique mercator, centred on Scotian Shelf rotated by 325 degrees
trawlable_units = tu
)
# specific selection params required for survey.db(DS="filter") data selection mechanism
p = aegis.survey::survey_parameters(
p=p,
selection=list(
biologicals=list(
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=p$groundfish_species_code )
),
survey=list(
data.source="groundfish",
yr = p$yrs, # time frame for comparison specified above
months=6:8,
# dyear = c(150,250)/365, # summer = which( (x>150) & (x<250) ) , spring = which( x<149 ), winter = which( x>251 )
settype = 1,
gear = c("Western IIA trawl", "Yankee #36 otter trawl"),
strata_toremove=c("Gulf", "Georges_Bank", "Spring", "Deep_Water"), # <<<<< strata to remove from standard strata-based analysis
polygon_enforce=TRUE,
ranged_data="dyear"
)
)
if (0) {
# used for DEBUGGING: access via strata_dataformat .. ie. directly from groundfish.db .. gscat
# gscat does not have the data corrections due to miscoding etc, vessel-species "catchability" corrections, etc
# .. but the totals are nearly identical to survey.db access
p$selection$biologicals$spec = groundfish_species_code
p$selection$biologicals$spec_bio = NULL
set = strata_dataformat( p=p ) # return values in kg or no per set
# dim(set) # [1] 1682 45
# sum(set$totwgt) # [1] 9683
# sum(set$totno) # [1] 15261
}
# ------------------------------------------------
# NOTE polygon areas of strata are predetermined in GSSTRATUM and used by "stratanal"
# This behaviour is mimicked in survey.db .. although it does not have to be
# .. The CAR variation computes surfaces directly from polygons
# Using this approach is better in that there is more filter control
# Results for the basic test cases are essentially identical to "stratanal" (via "strata_dataformat", above)
# but faster and more QA/QC done on the input data
set = aegis.survey::survey.db( p=p, DS="filter", add_groundfish_strata=TRUE ) # return values in kg or no per set
if (0) {
# the above merges based upon AUID's designated in groundfish tables. Alternatively one can use positions directly:
set = survey.db( p=p, DS="filter" )
# categorize Strata
sppoly = areal_units( areal_units_source="stratanal_polygons", areal_units_proj4string_planar_km=p$areal_units_proj4string_planar_km, areal_units_timeperiod="pre2014" )
sppoly$strata_to_keep = ifelse( as.character(sppoly$AUID) %in% strata_definitions( c("Gulf", "Georges_Bank", "Spring", "Deep_Water") ), FALSE, TRUE )
o = over( SpatialPoints( set[,c("lon", "lat")], sp::CRS(projection_proj4string("lonlat_wgs84")) ), spTransform(sppoly, sp::CRS(projection_proj4string("lonlat_wgs84")) ) ) # match each datum to an area
set$AUID = o$AUID
o = NULL
set = set[ which(!is.na(set$AUID)),]
}
# dim(set) # [1] 1684 39
# sum(set$totwgt) # [1] 9691
# sum(set$totno) # [1] 15263
# sum(set$totwgt_adjusted) # [1] 242838
# sum(set$totno_adjusted) # [1] 380823
# compute no of trawlable units for each stratum
# "strat" and "nh" are used by stratanal
set$strat = set$AUID
ft2m = 0.3048
m2km = 1/1000
nmi2mi = 1.1507794
mi2ft = 5280
standardtow_sakm2 = (41 * ft2m * m2km ) * ( 1.75 * nmi2mi * mi2ft * ft2m * m2km ) # surface area sampled by a standard tow in km^2 1.75 nm
# = 0.0405 and NOT 0.011801 .. where did this come from?
# set up trawlable units used in stratanal
set$nh = switch( p$trawlable_units,
sweptarea = as.numeric(set$au_sa_km2) * set$cf_cat, # convert strata area to trawlable units 41ft by 1.75 nm, divide area by sweptarea
standardtow = as.numeric(set$au_sa_km2) / standardtow_sakm2, # convert strata area to trawlable units 41ft by 1.75 nm, divide area by 0.011801
towdistance = as.numeric(set$au_sa_km2) / set$sa_towdistance # convert strata area to trawlable units 41ft by 1.75 nm, divide area by 0.011801
)
i = which(!is.finite(set$nh))
set$nh[i] = as.numeric(set$au_sa_km2[i]) / standardtow_sakm2 # override missing with "standard set" .. also if no trawlable_units set
# ------------------------------------------------
# Random stratified estimates from a faster variation of Michelle's code
results_basic = strata_timeseries(
set = set,
speciesname = p[["label"]],
yr = p$yrs,
variable ="totwgt",
alpha.t = 0.05, # confidence interval eg. 0.05 = 95%, 0.1 = 90%
alpha.b = 0.05,
nresamp = 1000,
prints=TRUE
)
(results_basic)
RES[,paste("stratanal", tu, sep="_")] = results_basic$pop.total[match(results_basic$year, RES$yr)]
# plot( stratanal ~ yr, data=RES, lty=5, lwd=4, col="red", type="b", ylim=c(0,8e8))
# lines ( stratanal ~ yr, data=RES, lty=5, lwd=4, col="red", type="b", ylim=c(0,8e8))
}
dev.new(width=11, height=7)
col = c("slategray", "turquoise", "darkorange" )
pch = c(20, 21, 22)
lty = c(1, 3, 4 )
lwd = c(4, 4, 4)
type =c("l", "l", "l")
plot( stratanal_standardtow ~ yr, data=RES, lty=lty[1], lwd=lwd[1], col=col[1], pch=pch[1], type=type[1], ylim=c(0,2.6e8), xlab="Year", ylab="kg")
lines( stratanal_towdistance ~ yr, data=RES, lty=lty[2], lwd=lwd[2], col=col[2], pch=pch[2], type=type[2])
lines( stratanal_sweptarea ~ yr, data=RES, lty=lty[3], lwd=lwd[3], col=col[3], pch=pch[3], type=type[3])
legend("topright", legend=c("Standard tow", "Length adjusted", "Length & width adjusted"), lty=lty, col=col, lwd=lwd )
dev.new(width=6, height=4)
hist( RES$stratanal_towdistance / RES$stratanal_standardtow, breaks=20 )
dev.new(width=6, height=4)
hist( RES$stratanal_sweptarea / RES$stratanal_standardtow, breaks=20 )
cor( RES[, c("stratanal_standardtow", "stratanal_towdistance", "stratanal_sweptarea")])
plot( RES[, c("stratanal_standardtow", "stratanal_towdistance", "stratanal_sweptarea")])
# ------------------------------------------------
# these are Michelle's results: (base access of gcat without correction factors for boat, species, etc)
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length
2.5% COD ATLANTIC 2017 14593959 totwgt_sd 3.4420 3.4258 3.61840 3.3099 3.5451 0.235210 21313 0.81003
2.5%34 COD ATLANTIC 2016 27531380 totwgt_sd 6.4932 6.3838 23.36500 6.0890 6.6869 0.597900 20779 0.89443
2.5%33 COD ATLANTIC 2015 8915342 totwgt_sd 2.1027 2.0970 0.17429 2.0716 2.1232 0.051683 24031 0.71914
2.5%32 COD ATLANTIC 2014 28570078 totwgt_sd 6.7382 6.8005 13.48700 6.5766 7.0328 0.456180 20416 0.88363
2.5%31 COD ATLANTIC 2013 12550459 totwgt_sd 2.9600 2.9837 1.13150 2.9189 3.0504 0.131470 24549 0.76574
2.5%30 COD ATLANTIC 2012 9538831 totwgt_sd 2.2497 2.2245 0.37251 2.1873 2.2630 0.075729 22789 0.76290
2.5%29 COD ATLANTIC 2011 35724538 totwgt_sd 8.4256 8.4033 20.51000 8.1265 8.6906 0.564150 24609 0.79815
2.5%28 COD ATLANTIC 2010 44532221 totwgt_sd 10.5030 10.3040 43.71900 9.9038 10.7220 0.817780 28273 0.83744
# ------------------------------------------------
# these are with "standard tow" assumptions:
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length dwao gini lower.ci.gini upper.ci.gini mean.3.yr median median.50
2.5% Cod summer standardtow 2017 14863259 totwgt 3.5136 3.4339 3.80320 3.3145 3.5572 0.242740 21451 0.77906
2.5%7 Cod summer standardtow 2016 21430734 totwgt 5.0662 5.1501 11.10100 4.9457 5.3587 0.412980 20681 0.84814
2.5%6 Cod summer standardtow 2015 8723439 totwgt 2.0622 2.0598 0.16459 2.0347 2.0851 0.050439 23705 0.65717
2.5%5 Cod summer standardtow 2014 27156331 totwgt 6.4197 6.4166 13.37500 6.1928 6.6459 0.453100 20786 0.83574
2.5%4 Cod summer standardtow 2013 12288438 totwgt 2.9050 2.9377 1.12560 2.8729 3.0045 0.131620 24411 0.73470
2.5%3 Cod summer standardtow 2012 9105517 totwgt 2.1525 2.1576 0.38184 2.1198 2.1957 0.075942 22465 0.73825
2.5%2 Cod summer standardtow 2011 34542306 totwgt 8.1657 8.1041 20.12700 7.8330 8.3863 0.553380 24584 0.77513
2.5%1 Cod summer standardtow 2010 42903020 totwgt 10.1420 10.4090 42.51400 10.0130 10.8180 0.805170 28360 0.82533
# ------------------------------------------------
# towed distance
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length dwao gini
2.5% Cod summer towdistance 2017 15025543 totwgt 3.4420 3.4205 3.76150 3.3030 3.5434 0.240360 22219 0.77524
2.5%7 Cod summer towdistance 2016 23732430 totwgt 5.5022 5.4666 14.00500 5.2375 5.7001 0.462570 21266 0.84436
2.5%6 Cod summer towdistance 2015 8798976 totwgt 2.0690 2.0732 0.15893 2.0490 2.0982 0.049255 23901 0.65307
2.5%5 Cod summer towdistance 2014 28503738 totwgt 6.5806 6.6708 12.70500 6.4516 6.8923 0.440700 21401 0.83786
2.5%4 Cod summer towdistance 2013 12434510 totwgt 2.8474 2.8555 1.02700 2.7944 2.9199 0.125560 25127 0.73675
2.5%3 Cod summer towdistance 2012 9340895 totwgt 2.1474 2.1441 0.37216 2.1066 2.1824 0.075768 23436 0.73386
2.5%2 Cod summer towdistance 2011 35721843 totwgt 8.1870 8.0592 21.59400 7.7759 8.3539 0.578040 25474 0.77066
2.5%1 Cod summer towdistance 2010 43790809 totwgt 9.9849 9.8188 43.78700 9.4161 10.2420 0.825920 29684 0.81764
# ------------------------------------------------
# sweptarea
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length dwao
2.5% Cod summer sweptarea 2017 14584703 totwgt 3.5108 3.5984 4.12750 3.4741 3.7256 0.251540 20961
2.5%7 Cod summer sweptarea 2016 20677264 totwgt 5.0506 5.0506 11.32400 4.8465 5.2649 0.418400 19806
2.5%6 Cod summer sweptarea 2015 7397592 totwgt 1.9220 1.9240 0.13729 1.9011 1.9471 0.045981 20763
2.5%5 Cod summer sweptarea 2014 25264103 totwgt 6.5155 6.5552 14.94200 6.3205 6.7999 0.479430 18288
2.5%4 Cod summer sweptarea 2013 10290871 totwgt 2.7651 2.7570 1.12270 2.6915 2.8227 0.131200 20251
2.5%3 Cod summer sweptarea 2012 8839376 totwgt 2.1085 2.1188 0.36425 2.0816 2.1566 0.074987 22206
2.5%2 Cod summer sweptarea 2011 34866336 totwgt 8.4871 8.0913 24.96300 7.7888 8.4061 0.617280 23648
2.5%1 Cod summer sweptarea 2010 45648142 totwgt 10.7420 10.6010 52.95200 10.1560 11.0630 0.906710 28503
# TODO basic corelations and plots, summarizing the above
set$strata_year = paste( set$AUID, set$yr, sep=".")
nn = applySummary( set[, c("strata_year", "totno")] )
V = expand.grid( AUID=levels(set$AUID), yr=sort( unique(set$yr) ) )
V$strata_year = paste( V$AUID, V$yr, sep=".")
V = merge( V, nn, by="strata_year", all.x=TRUE, all.y=FALSE, suffixes=c("", ".totno") )
dev.new(); plot( log(totno.mean) ~ log(totno.sd), V ); abline(0,1)
### end
jae0/ecofishmod documentation built on July 6, 2020, 5:47 a.m.
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# ------------------------------------------------
# Atlantic cod comparison of naive strata-based averages
# This replicates standard groundfish strata-based estimation of means and totals
# "standard" random-stratified estimation functions (based on stratanal and bootstrap estimation techniques )
# derived from numerous authors .. this code has been optimized a bit more for clarity and speed by JSC
# ------------------------------------------------
# load data environment
RES = data.frame(yr=1970:2017) # collect model comparisons in this data frame
if (0) {
fn = file.path( getwd(), "RES.rdata" )
# save(RES, file=fn)
# load(fn)
}
yrs =2000:2018
for (tu in c( "standardtow", "towdistance", "sweptarea") ) {
# construct basic parameter list defining the main characteristics of the study and some plotting params
p = carstm::carstm_parameters(
label ="Atlantic cod summer standardtow",
speciesname = "Atlantic_cod",
groundfish_species_code = 10, # 10= cod
yrs = yrs,
areal_units_timeperiod = "pre2014", # "pre2014" for older
areal_units_proj4string_planar_km = projection_proj4string("omerc_nova_scotia"), # oblique mercator, centred on Scotian Shelf rotated by 325 degrees
trawlable_units = tu
)
# specific selection params required for survey.db(DS="filter") data selection mechanism
p = aegis.survey::survey_parameters(
p=p,
selection=list(
biologicals=list(
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=p$groundfish_species_code )
),
survey=list(
data.source="groundfish",
yr = p$yrs, # time frame for comparison specified above
months=6:8,
# dyear = c(150,250)/365, # summer = which( (x>150) & (x<250) ) , spring = which( x<149 ), winter = which( x>251 )
settype = 1,
gear = c("Western IIA trawl", "Yankee #36 otter trawl"),
strata_toremove=c("Gulf", "Georges_Bank", "Spring", "Deep_Water"), # <<<<< strata to remove from standard strata-based analysis
polygon_enforce=TRUE,
ranged_data="dyear"
)
)
if (0) {
# used for DEBUGGING: access via strata_dataformat .. ie. directly from groundfish.db .. gscat
# gscat does not have the data corrections due to miscoding etc, vessel-species "catchability" corrections, etc
# .. but the totals are nearly identical to survey.db access
p$selection$biologicals$spec = groundfish_species_code
p$selection$biologicals$spec_bio = NULL
set = strata_dataformat( p=p ) # return values in kg or no per set
# dim(set) # [1] 1682 45
# sum(set$totwgt) # [1] 9683
# sum(set$totno) # [1] 15261
}
# ------------------------------------------------
# NOTE polygon areas of strata are predetermined in GSSTRATUM and used by "stratanal"
# This behaviour is mimicked in survey.db .. although it does not have to be
# .. The CAR variation computes surfaces directly from polygons
# Using this approach is better in that there is more filter control
# Results for the basic test cases are essentially identical to "stratanal" (via "strata_dataformat", above)
# but faster and more QA/QC done on the input data
set = aegis.survey::survey.db( p=p, DS="filter", add_groundfish_strata=TRUE ) # return values in kg or no per set
if (0) {
# the above merges based upon AUID's designated in groundfish tables. Alternatively one can use positions directly:
set = survey.db( p=p, DS="filter" )
# categorize Strata
sppoly = areal_units( areal_units_source="stratanal_polygons", areal_units_proj4string_planar_km=p$areal_units_proj4string_planar_km, areal_units_timeperiod="pre2014" )
sppoly$strata_to_keep = ifelse( as.character(sppoly$AUID) %in% strata_definitions( c("Gulf", "Georges_Bank", "Spring", "Deep_Water") ), FALSE, TRUE )
o = over( SpatialPoints( set[,c("lon", "lat")], sp::CRS(projection_proj4string("lonlat_wgs84")) ), spTransform(sppoly, sp::CRS(projection_proj4string("lonlat_wgs84")) ) ) # match each datum to an area
set$AUID = o$AUID
o = NULL
set = set[ which(!is.na(set$AUID)),]
}
# dim(set) # [1] 1684 39
# sum(set$totwgt) # [1] 9691
# sum(set$totno) # [1] 15263
# sum(set$totwgt_adjusted) # [1] 242838
# sum(set$totno_adjusted) # [1] 380823
# compute no of trawlable units for each stratum
# "strat" and "nh" are used by stratanal
set$strat = set$AUID
ft2m = 0.3048
m2km = 1/1000
nmi2mi = 1.1507794
mi2ft = 5280
standardtow_sakm2 = (41 * ft2m * m2km ) * ( 1.75 * nmi2mi * mi2ft * ft2m * m2km ) # surface area sampled by a standard tow in km^2 1.75 nm
# = 0.0405 and NOT 0.011801 .. where did this come from?
# set up trawlable units used in stratanal
set$nh = switch( p$trawlable_units,
sweptarea = as.numeric(set$au_sa_km2) * set$cf_cat, # convert strata area to trawlable units 41ft by 1.75 nm, divide area by sweptarea
standardtow = as.numeric(set$au_sa_km2) / standardtow_sakm2, # convert strata area to trawlable units 41ft by 1.75 nm, divide area by 0.011801
towdistance = as.numeric(set$au_sa_km2) / set$sa_towdistance # convert strata area to trawlable units 41ft by 1.75 nm, divide area by 0.011801
)
i = which(!is.finite(set$nh))
set$nh[i] = as.numeric(set$au_sa_km2[i]) / standardtow_sakm2 # override missing with "standard set" .. also if no trawlable_units set
# ------------------------------------------------
# Random stratified estimates from a faster variation of Michelle's code
results_basic = strata_timeseries(
set = set,
speciesname = p[["label"]],
yr = p$yrs,
variable ="totwgt",
alpha.t = 0.05, # confidence interval eg. 0.05 = 95%, 0.1 = 90%
alpha.b = 0.05,
nresamp = 1000,
prints=TRUE
)
(results_basic)
RES[,paste("stratanal", tu, sep="_")] = results_basic$pop.total[match(results_basic$year, RES$yr)]
# plot( stratanal ~ yr, data=RES, lty=5, lwd=4, col="red", type="b", ylim=c(0,8e8))
# lines ( stratanal ~ yr, data=RES, lty=5, lwd=4, col="red", type="b", ylim=c(0,8e8))
}
dev.new(width=11, height=7)
col = c("slategray", "turquoise", "darkorange" )
pch = c(20, 21, 22)
lty = c(1, 3, 4 )
lwd = c(4, 4, 4)
type =c("l", "l", "l")
plot( stratanal_standardtow ~ yr, data=RES, lty=lty[1], lwd=lwd[1], col=col[1], pch=pch[1], type=type[1], ylim=c(0,2.6e8), xlab="Year", ylab="kg")
lines( stratanal_towdistance ~ yr, data=RES, lty=lty[2], lwd=lwd[2], col=col[2], pch=pch[2], type=type[2])
lines( stratanal_sweptarea ~ yr, data=RES, lty=lty[3], lwd=lwd[3], col=col[3], pch=pch[3], type=type[3])
legend("topright", legend=c("Standard tow", "Length adjusted", "Length & width adjusted"), lty=lty, col=col, lwd=lwd )
dev.new(width=6, height=4)
hist( RES$stratanal_towdistance / RES$stratanal_standardtow, breaks=20 )
dev.new(width=6, height=4)
hist( RES$stratanal_sweptarea / RES$stratanal_standardtow, breaks=20 )
cor( RES[, c("stratanal_standardtow", "stratanal_towdistance", "stratanal_sweptarea")])
plot( RES[, c("stratanal_standardtow", "stratanal_towdistance", "stratanal_sweptarea")])
# ------------------------------------------------
# these are Michelle's results: (base access of gcat without correction factors for boat, species, etc)
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length
2.5% COD ATLANTIC 2017 14593959 totwgt_sd 3.4420 3.4258 3.61840 3.3099 3.5451 0.235210 21313 0.81003
2.5%34 COD ATLANTIC 2016 27531380 totwgt_sd 6.4932 6.3838 23.36500 6.0890 6.6869 0.597900 20779 0.89443
2.5%33 COD ATLANTIC 2015 8915342 totwgt_sd 2.1027 2.0970 0.17429 2.0716 2.1232 0.051683 24031 0.71914
2.5%32 COD ATLANTIC 2014 28570078 totwgt_sd 6.7382 6.8005 13.48700 6.5766 7.0328 0.456180 20416 0.88363
2.5%31 COD ATLANTIC 2013 12550459 totwgt_sd 2.9600 2.9837 1.13150 2.9189 3.0504 0.131470 24549 0.76574
2.5%30 COD ATLANTIC 2012 9538831 totwgt_sd 2.2497 2.2245 0.37251 2.1873 2.2630 0.075729 22789 0.76290
2.5%29 COD ATLANTIC 2011 35724538 totwgt_sd 8.4256 8.4033 20.51000 8.1265 8.6906 0.564150 24609 0.79815
2.5%28 COD ATLANTIC 2010 44532221 totwgt_sd 10.5030 10.3040 43.71900 9.9038 10.7220 0.817780 28273 0.83744
# ------------------------------------------------
# these are with "standard tow" assumptions:
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length dwao gini lower.ci.gini upper.ci.gini mean.3.yr median median.50
2.5% Cod summer standardtow 2017 14863259 totwgt 3.5136 3.4339 3.80320 3.3145 3.5572 0.242740 21451 0.77906
2.5%7 Cod summer standardtow 2016 21430734 totwgt 5.0662 5.1501 11.10100 4.9457 5.3587 0.412980 20681 0.84814
2.5%6 Cod summer standardtow 2015 8723439 totwgt 2.0622 2.0598 0.16459 2.0347 2.0851 0.050439 23705 0.65717
2.5%5 Cod summer standardtow 2014 27156331 totwgt 6.4197 6.4166 13.37500 6.1928 6.6459 0.453100 20786 0.83574
2.5%4 Cod summer standardtow 2013 12288438 totwgt 2.9050 2.9377 1.12560 2.8729 3.0045 0.131620 24411 0.73470
2.5%3 Cod summer standardtow 2012 9105517 totwgt 2.1525 2.1576 0.38184 2.1198 2.1957 0.075942 22465 0.73825
2.5%2 Cod summer standardtow 2011 34542306 totwgt 8.1657 8.1041 20.12700 7.8330 8.3863 0.553380 24584 0.77513
2.5%1 Cod summer standardtow 2010 42903020 totwgt 10.1420 10.4090 42.51400 10.0130 10.8180 0.805170 28360 0.82533
# ------------------------------------------------
# towed distance
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length dwao gini
2.5% Cod summer towdistance 2017 15025543 totwgt 3.4420 3.4205 3.76150 3.3030 3.5434 0.240360 22219 0.77524
2.5%7 Cod summer towdistance 2016 23732430 totwgt 5.5022 5.4666 14.00500 5.2375 5.7001 0.462570 21266 0.84436
2.5%6 Cod summer towdistance 2015 8798976 totwgt 2.0690 2.0732 0.15893 2.0490 2.0982 0.049255 23901 0.65307
2.5%5 Cod summer towdistance 2014 28503738 totwgt 6.5806 6.6708 12.70500 6.4516 6.8923 0.440700 21401 0.83786
2.5%4 Cod summer towdistance 2013 12434510 totwgt 2.8474 2.8555 1.02700 2.7944 2.9199 0.125560 25127 0.73675
2.5%3 Cod summer towdistance 2012 9340895 totwgt 2.1474 2.1441 0.37216 2.1066 2.1824 0.075768 23436 0.73386
2.5%2 Cod summer towdistance 2011 35721843 totwgt 8.1870 8.0592 21.59400 7.7759 8.3539 0.578040 25474 0.77066
2.5%1 Cod summer towdistance 2010 43790809 totwgt 9.9849 9.8188 43.78700 9.4161 10.2420 0.825920 29684 0.81764
# ------------------------------------------------
# sweptarea
speciesname year pop.total variable orig.mean boot.mean var.boot.mean lower.ci upper.ci length dwao
2.5% Cod summer sweptarea 2017 14584703 totwgt 3.5108 3.5984 4.12750 3.4741 3.7256 0.251540 20961
2.5%7 Cod summer sweptarea 2016 20677264 totwgt 5.0506 5.0506 11.32400 4.8465 5.2649 0.418400 19806
2.5%6 Cod summer sweptarea 2015 7397592 totwgt 1.9220 1.9240 0.13729 1.9011 1.9471 0.045981 20763
2.5%5 Cod summer sweptarea 2014 25264103 totwgt 6.5155 6.5552 14.94200 6.3205 6.7999 0.479430 18288
2.5%4 Cod summer sweptarea 2013 10290871 totwgt 2.7651 2.7570 1.12270 2.6915 2.8227 0.131200 20251
2.5%3 Cod summer sweptarea 2012 8839376 totwgt 2.1085 2.1188 0.36425 2.0816 2.1566 0.074987 22206
2.5%2 Cod summer sweptarea 2011 34866336 totwgt 8.4871 8.0913 24.96300 7.7888 8.4061 0.617280 23648
2.5%1 Cod summer sweptarea 2010 45648142 totwgt 10.7420 10.6010 52.95200 10.1560 11.0630 0.906710 28503
# TODO basic corelations and plots, summarizing the above
set$strata_year = paste( set$AUID, set$yr, sep=".")
nn = applySummary( set[, c("strata_year", "totno")] )
V = expand.grid( AUID=levels(set$AUID), yr=sort( unique(set$yr) ) )
V$strata_year = paste( V$AUID, V$yr, sep=".")
V = merge( V, nn, by="strata_year", all.x=TRUE, all.y=FALSE, suffixes=c("", ".totno") )
dev.new(); plot( log(totno.mean) ~ log(totno.sd), V ); abline(0,1)
### end
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