R/runFlukeMSE.R
In ahart1/flukeclimatemse: Run Climate Fluke MSE
Defines functions
runFlukeMSE
# This script contains the run function which loops over all sub-model components to run the fluke MSE
# ToDo
# search ??? for unresolved questions/issues
# search Placeholder to connect data between different model components
#??? this is the input I need to run this model
# Recruitment timeseries (000s) from 66th Northeast Regional Stock Assessment Workshop Assessment Report (NEFSC 2019) Table A87, page 238, years 1982-2017.
RData <- c(81955, 102427, 46954, 78263, 81397, 53988, 12474, 36963, 44019, 47704, 47264, 43928, 58403, 78348, 59520, 52374, 54518, 44100, 60551, 64979, 67860, 50131, 71270, 40634, 48153, 52646, 62460, 73747, 51331, 31296, 35187, 36719, 42271, 29833, 35853, 42415)
PopLatitude <- c(39.24292, 39.56186, 39.49013, 39.63467, 39.33152, 39.39787, 39.66504, 39.86545, 39.88054, 39.39388,
39.83786, 39.82090, 40.07682, 40.07937, 39.81917, 39.93204, 39.84950, 39.75041, 39.84984, 40.27842,
40.12037, 39.69240, 39.63471, 39.55760, 39.65226) # Data from 1993-2017
#' @title Run through entire MSE loop for fluke
#'
#' @description
#'
#' @param Nproj An integer specifying the length of the projection forward in time.
#' @param SimulationOutput A string containing the file path for the output file
####### From operating model
#' @param Abundance A vector of length two containing historic abundance (number of fish) in the last two years of the provided timeseries below (serves as starting point for abundance projections in Operating Model)
#' @param Recruitment A vector of historic recruitments which are updated by the operating model throughout each simulation
#' @param R_devs A vector of historic recruitment deviations which are updated by the operating model throughout each simulation from the operating model
#' @param PopLat A vector of latitudes for population historic center of gravity (1993-2017 to match MRIP data used in fishermenresponse.R)
####### From stock assessment model
#' @param RecreationCatch A matrix containing recreational catch observations by year (rows) and state (columns), no default.
#' @param RecreationDiscard A matrix containing recreational discards observations by year (rows) and state (columns), no default.
#' @param CommercialFishing A matrix of combined commercial catch and discards observations by year (rows) and state (columns), no default.
#' @param SurveyObservation A vector of survey abundance combined across states, no default.
#' @param Survey_SD A vector of survey standard deviations combined across states, no default.
####### From management model
#' @param DecisionArea A string to specify the management decision areas for the simulation: specify "CoastWide", "StatesIndependent", "CTandNYGroup", "CTandNYandNJGroup", no default.
#' "CoastWide" specifies one set of management regulations for all states in which summer flounder are caught.
#' "StatesIndependent" specifies separate management regulations for each state in which summer flounder are caught.
#' "CTandNYGroup" specifies separate management regulations for Massachusetts/Rhode Island/combined Connecticut and New York/New Jersey/combined Delaware, Maryland, and Virginia/North Carolina
#' "CTandNYandNJGroup" specifies separate management regulations for Massachusetts/Rhode Island/combined Connecticut, New York, and New Jersey/combined Delaware, Maryland, and Virginia/North Carolina
#' @param QuotaMethod A string specifying which method should be used to allocate quota to decision areas: "Historic" or "BioAvailability", no default.
#' "Historic" allocates quota based on historic participation in the recreational summer flounder fishery from 1980-1988 as is done in the current (2018) stock assessment. ??? double check dates
#' "BioAvailability" allocates quota based on biomass availability by state as determined ??? update once availability determined correctly (not based on OM, use OM+error "survey")
#' @param BagSize A matrix of management settings for bag size by year (rows) and state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
#' @param MinSize A matrix of management settings for minimum landing size by year (rows) and state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
#' @param SeasonLength A matrix of management settings for season length (days) by year (rows) and state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
#' @param InputMngmtMethod A string specifying what input controls should be adjusted selected from the following options: "AdjustBagSize", "AdjustMinSize", "AdjustSeason", "AdjustAll", "AdjustMixed", and "AdjustSpecific". No default.
#' "AdjustBagSize" Specifies that only bag size should be adjusted to alter recreational catch, minimum landing size and season length remain unchanged throughout simulated projection.
#' "AdjustMinSize" Specifies that only minimum landing size should be adjusted to alter recreational catch, bag size and season length remain unchanged throughout simulated projection.
#' "AdjustSeason" Specifies that only season length should be adjusted to alter recreational catch, bag size and minimum landing size remain unchanged throughout simulated projection.
#' "AdjustAll" Specifies that bag size, minimum landing size, and season length should all be be adjusted to alter recreational catch.
#' "AdjustMixed" Randomly select to adjust between 0 and 3 input controls for each state & then randomly select that number of input controls from: bag size, minimum size, and season length to implement together in each state, this setting only functions if DecisionArea == "StatesIndependent".
#' "AdjustSpecific" Specifies that bag size, minimum landing size, and season length be fixed at specified settings for entire projection to test the settings of interest, requires an additional argument: adjustspecific.
#' @param adjustspecific Optional matrix required by InputMngmt = "AdjustSpecific" setting, contains specific settings for bag size, minimum landing size, and season length (rows labeled: "bagsize" "minsize" "seasonlength") by state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
###### From fishermen response model
#' @param FishermanGAMData Matrix containing the following columns "Year","State","RecCatch","MinSize","BagSize","SeasonLength","Effort","SSB","BioAvailable","PopLat" #??? needs updating after ICES with availability instead of latitude proxy
#'
#' @return
#'
#'
#' @examples
#'
runFlukeMSE <- function(Nproj = 1){
##### Set up storage
# Operating model output that does not update existing input data set
StoreOMBiomass <- NULL # OM biomass timeseries
StoreOMRdevs <- NULL # OM recruitment deviation timeseries
StoreHabitatAvailability <- NULL
# Stock assessment output at end of Nproj length projection
StoreTotalBiomass <- NULL # Timeseries of total biomass summed across states
StoreRecruitment <- NULL # Calculated recruitment timeseries
StoreMeanRecruitment <- NULL # Mean recruitment
StoreRecDevs <- NULL # Recruitment deviation timeseries
StoreCatchPred <- NULL # Predicted catch timeseries
StoreSurvival <- NULL # Survival timeseries
StoreF_vals <- NULL # Catch F timeseries estimates at end of Nproj projection
# Store management output
StoreCommercialQuota <- NULL # Vector containing commercial quota from projection
StoreAreaQuotas <- NULL # Matrix containing area quota from projection
StoreInputSettings <- NULL # Matrix containing BagSize, MinSize, SeasonLength over time(rows)
# Store fishermen response output
##### Initial calculations, constants across all simulations
# Fit GAM to describe Fishermen Response, output is passed to fishermenresponse()
# ??? the GAM used here needs to be updated after ICES with habitat availability rather than proxy
FishermanGAM <- mgcv::gam(RecCatch ~ State*PopLat + s(MinSize) + s(BagSize,k=8) + s(SeasonLength), data = FishermanGAMData)
# Calculate mean Recruitment from historic dataset
meanR <- mean(Recruitment)
for(iyear in 1:Nproj){
##### Operating Model ##########################################################################################
# Sum rec catch, rec discards, commercial catch in last year across states
OMcatchinput <- c(sum(RecreationCatch[nrow(RecreationCatch),]), sum(RecreationDiscard[nrow(RecreationDiscard),]), sum(CommercialFishing[nrow(CommercialFishing),]))
# Don't need # OMcatchinput_tminusone <- c(sum(RecreationCatch[(nrow(RecreationCatch)-1),]), sum(RecreationDiscard[(nrow(RecreationDiscard)-1),]), sum(CommercialFishing[(nrow(CommercialFishing)-1),]))
OM_Output <- flukeOM(Catch = OMcatchinput,
Catch_tminusone = OMcatchinput_tminusone,
N_abund = Abundance[length(Abundance)],
N_tilda_tminusone = Abundance[(length(Abundance)-1)],
Recruitment = meanRecruitment,
R_devs = R_devs[length(R_devs)],
PopLat = PopLat)
# Update OM biomass, survey, and availability from OM output
StoreOMBiomass <- c(StoreOMBiomass, OM_Output$OM_Biomass)
Abundance <- c(Abundance, OM_Output$OM_Abundance)
Recruitment <- c(Recruitment, OM_Output$OM_Rproj)
StoreOMRdevs <- c(R_devs, OM_Output$OM_Rdevproj)
SurveyObservation <- c(SurveyObservation, OM_Output$OM_Survey)
SurveySD <- c(SurveySD, OM_Output$OM_surveyStdDev)
# StoreHabitatAvailability <- rbind(StoreHabitatAvailability, OM_Output$HabitatAvailable) #??? update after ICES
PopLat <- OM_Output$OM_PopLat # Updated vector of latitudes for population center of gravity
##### Stock assessment model ##########################################################################################
assessOutput <- stockassess(Rec_catch = sum(RecreationCatch[nrow(RecreationCatch),]),
Rec_disc = sum(RecreationDiscard[nrow(RecreationDiscard),]),
Com_catdisc = sum(CommercialFishing[nrow(CommercialFishing),]),
Survey_obs = SurveyObservation, # Historic survey series updated by OM Don't know how/what to combine, append OM production here
Survey_SD = SurveySD, # Historic survey series updated by OM
Recruits = Recruitment) # Historic recruitment updated by OM
# Update biomass, catch, and survey settings from stock assessment model output, outputs from the end of the projection will be stored in simulation output
StoreTotalBiomass <- assessOutput$biomass # Total biomass summed across states
StoreRecruitmentPred <- assessOutput$recruitment # Predicted recruitment timeseries
StoreMeanRecruitment <- assessOutput$R_mean # Mean recruitment
StoreRecDevs <- assessOutput$R_devs # Recruitment deviation timeseries
StoreCatchPred <- assessOutput$catch_pred # Predicted catch timeseries
StoreSurvival <- assessOutput$survival # Timeseries of survival
StoreF_vals <- assessOutput$F_vals # Catch F estimate timeseries
##### Management procedure model ##########################################################################################
MngmtOutput <- mngmtprocedure(DecisionArea = DecisionArea,
QuotaMethod = QuotaMethod,
Availability = OM_Output$HabitatAvailable, # Habitat availability by state, stored in StoreHabitatAvailibility
TotalBiomass = assessOutput$biomass, # ??? need to calculate total quota as part of the management procedure model
iYear = iyear,
CatchObs = RecreationCatch,
BagSize = BagSize,
MinSize = MinSize,
SeasonLength = SeasonLength,
InputMngmtMethod = InputMngmtMethod)
# Update management settings from management procedure model output
BagSize <- MngmtOutput$BagSize
MinSize <- MngmtOutput$MinSize
SeasonLength <- MngmtOutput$SeasonLength
StoreInputSettings <- rbind(StoreInputSettings, c(BagSize, MinSize, SeasonLength)) # Matrix containing BagSize, MinSize, SeasonLength settings over time(rows)
StoreAreaQuotas <- rbind(StoreAreaQuotas, MngmtOutput$AreaQuota) # Matrix containing area quota from projection
StoreCommercialQuota <- rbind(StoreCommercialQuota, MngmtOutput$CommercialQuota) # Vector containing commercial quota from projection
# Update vector of combined commercial discards and landings, assume that entire quota is landed
CommercialFishing <- c(CommercialFishing, MngmtOutput$CommercialQuota) # do commercial discards need to be calculated & added separately or are discards included in quota ???
##### Fishermen response model ##########################################################################################
responseOutput <- fishermenresponse(GAM = FishermanGAM,
MinSize = MinSize,
BagSize = BagSize,
SeasonLength = SeasonLength,
PopLat = PopLat) # ??? this needs updating after ICES
# Update catch & discards
RecreationCatch <- rbind(RecreationCatch, responseOutput$reccatPlaceholder)
RecreationDiscard <- rbind(RecreationDiscard, responseOutput$recdiscPlaceholder)
} # End of loop over projection years
##### Save simulation settings and outputs ##########################################################################################
OutputList <- list()
OutputJSON <- toJSON(OutputList)
# write(prettify(SimulationOutput), file = SimulationOutput) # ??? do I need prettify?
write(SimulationOutput, file = Simulationoutput)
} # End of function
ahart1/flukeclimatemse documentation built on Oct. 5, 2020, 4:42 p.m.
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Defines functions runFlukeMSE
# This script contains the run function which loops over all sub-model components to run the fluke MSE
# ToDo
# search ??? for unresolved questions/issues
# search Placeholder to connect data between different model components
#??? this is the input I need to run this model
# Recruitment timeseries (000s) from 66th Northeast Regional Stock Assessment Workshop Assessment Report (NEFSC 2019) Table A87, page 238, years 1982-2017.
RData <- c(81955, 102427, 46954, 78263, 81397, 53988, 12474, 36963, 44019, 47704, 47264, 43928, 58403, 78348, 59520, 52374, 54518, 44100, 60551, 64979, 67860, 50131, 71270, 40634, 48153, 52646, 62460, 73747, 51331, 31296, 35187, 36719, 42271, 29833, 35853, 42415)
PopLatitude <- c(39.24292, 39.56186, 39.49013, 39.63467, 39.33152, 39.39787, 39.66504, 39.86545, 39.88054, 39.39388,
39.83786, 39.82090, 40.07682, 40.07937, 39.81917, 39.93204, 39.84950, 39.75041, 39.84984, 40.27842,
40.12037, 39.69240, 39.63471, 39.55760, 39.65226) # Data from 1993-2017
#' @title Run through entire MSE loop for fluke
#'
#' @description
#'
#' @param Nproj An integer specifying the length of the projection forward in time.
#' @param SimulationOutput A string containing the file path for the output file
####### From operating model
#' @param Abundance A vector of length two containing historic abundance (number of fish) in the last two years of the provided timeseries below (serves as starting point for abundance projections in Operating Model)
#' @param Recruitment A vector of historic recruitments which are updated by the operating model throughout each simulation
#' @param R_devs A vector of historic recruitment deviations which are updated by the operating model throughout each simulation from the operating model
#' @param PopLat A vector of latitudes for population historic center of gravity (1993-2017 to match MRIP data used in fishermenresponse.R)
####### From stock assessment model
#' @param RecreationCatch A matrix containing recreational catch observations by year (rows) and state (columns), no default.
#' @param RecreationDiscard A matrix containing recreational discards observations by year (rows) and state (columns), no default.
#' @param CommercialFishing A matrix of combined commercial catch and discards observations by year (rows) and state (columns), no default.
#' @param SurveyObservation A vector of survey abundance combined across states, no default.
#' @param Survey_SD A vector of survey standard deviations combined across states, no default.
####### From management model
#' @param DecisionArea A string to specify the management decision areas for the simulation: specify "CoastWide", "StatesIndependent", "CTandNYGroup", "CTandNYandNJGroup", no default.
#' "CoastWide" specifies one set of management regulations for all states in which summer flounder are caught.
#' "StatesIndependent" specifies separate management regulations for each state in which summer flounder are caught.
#' "CTandNYGroup" specifies separate management regulations for Massachusetts/Rhode Island/combined Connecticut and New York/New Jersey/combined Delaware, Maryland, and Virginia/North Carolina
#' "CTandNYandNJGroup" specifies separate management regulations for Massachusetts/Rhode Island/combined Connecticut, New York, and New Jersey/combined Delaware, Maryland, and Virginia/North Carolina
#' @param QuotaMethod A string specifying which method should be used to allocate quota to decision areas: "Historic" or "BioAvailability", no default.
#' "Historic" allocates quota based on historic participation in the recreational summer flounder fishery from 1980-1988 as is done in the current (2018) stock assessment. ??? double check dates
#' "BioAvailability" allocates quota based on biomass availability by state as determined ??? update once availability determined correctly (not based on OM, use OM+error "survey")
#' @param BagSize A matrix of management settings for bag size by year (rows) and state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
#' @param MinSize A matrix of management settings for minimum landing size by year (rows) and state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
#' @param SeasonLength A matrix of management settings for season length (days) by year (rows) and state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
#' @param InputMngmtMethod A string specifying what input controls should be adjusted selected from the following options: "AdjustBagSize", "AdjustMinSize", "AdjustSeason", "AdjustAll", "AdjustMixed", and "AdjustSpecific". No default.
#' "AdjustBagSize" Specifies that only bag size should be adjusted to alter recreational catch, minimum landing size and season length remain unchanged throughout simulated projection.
#' "AdjustMinSize" Specifies that only minimum landing size should be adjusted to alter recreational catch, bag size and season length remain unchanged throughout simulated projection.
#' "AdjustSeason" Specifies that only season length should be adjusted to alter recreational catch, bag size and minimum landing size remain unchanged throughout simulated projection.
#' "AdjustAll" Specifies that bag size, minimum landing size, and season length should all be be adjusted to alter recreational catch.
#' "AdjustMixed" Randomly select to adjust between 0 and 3 input controls for each state & then randomly select that number of input controls from: bag size, minimum size, and season length to implement together in each state, this setting only functions if DecisionArea == "StatesIndependent".
#' "AdjustSpecific" Specifies that bag size, minimum landing size, and season length be fixed at specified settings for entire projection to test the settings of interest, requires an additional argument: adjustspecific.
#' @param adjustspecific Optional matrix required by InputMngmt = "AdjustSpecific" setting, contains specific settings for bag size, minimum landing size, and season length (rows labeled: "bagsize" "minsize" "seasonlength") by state (columns in the following order: MA, RI, CT, NY, NJ, DE, MD, VA, NC), no default.
###### From fishermen response model
#' @param FishermanGAMData Matrix containing the following columns "Year","State","RecCatch","MinSize","BagSize","SeasonLength","Effort","SSB","BioAvailable","PopLat" #??? needs updating after ICES with availability instead of latitude proxy
#'
#' @return
#'
#'
#' @examples
#'
runFlukeMSE <- function(Nproj = 1){
##### Set up storage
# Operating model output that does not update existing input data set
StoreOMBiomass <- NULL # OM biomass timeseries
StoreOMRdevs <- NULL # OM recruitment deviation timeseries
StoreHabitatAvailability <- NULL
# Stock assessment output at end of Nproj length projection
StoreTotalBiomass <- NULL # Timeseries of total biomass summed across states
StoreRecruitment <- NULL # Calculated recruitment timeseries
StoreMeanRecruitment <- NULL # Mean recruitment
StoreRecDevs <- NULL # Recruitment deviation timeseries
StoreCatchPred <- NULL # Predicted catch timeseries
StoreSurvival <- NULL # Survival timeseries
StoreF_vals <- NULL # Catch F timeseries estimates at end of Nproj projection
# Store management output
StoreCommercialQuota <- NULL # Vector containing commercial quota from projection
StoreAreaQuotas <- NULL # Matrix containing area quota from projection
StoreInputSettings <- NULL # Matrix containing BagSize, MinSize, SeasonLength over time(rows)
# Store fishermen response output
##### Initial calculations, constants across all simulations
# Fit GAM to describe Fishermen Response, output is passed to fishermenresponse()
# ??? the GAM used here needs to be updated after ICES with habitat availability rather than proxy
FishermanGAM <- mgcv::gam(RecCatch ~ State*PopLat + s(MinSize) + s(BagSize,k=8) + s(SeasonLength), data = FishermanGAMData)
# Calculate mean Recruitment from historic dataset
meanR <- mean(Recruitment)
for(iyear in 1:Nproj){
##### Operating Model ##########################################################################################
# Sum rec catch, rec discards, commercial catch in last year across states
OMcatchinput <- c(sum(RecreationCatch[nrow(RecreationCatch),]), sum(RecreationDiscard[nrow(RecreationDiscard),]), sum(CommercialFishing[nrow(CommercialFishing),]))
# Don't need # OMcatchinput_tminusone <- c(sum(RecreationCatch[(nrow(RecreationCatch)-1),]), sum(RecreationDiscard[(nrow(RecreationDiscard)-1),]), sum(CommercialFishing[(nrow(CommercialFishing)-1),]))
OM_Output <- flukeOM(Catch = OMcatchinput,
Catch_tminusone = OMcatchinput_tminusone,
N_abund = Abundance[length(Abundance)],
N_tilda_tminusone = Abundance[(length(Abundance)-1)],
Recruitment = meanRecruitment,
R_devs = R_devs[length(R_devs)],
PopLat = PopLat)
# Update OM biomass, survey, and availability from OM output
StoreOMBiomass <- c(StoreOMBiomass, OM_Output$OM_Biomass)
Abundance <- c(Abundance, OM_Output$OM_Abundance)
Recruitment <- c(Recruitment, OM_Output$OM_Rproj)
StoreOMRdevs <- c(R_devs, OM_Output$OM_Rdevproj)
SurveyObservation <- c(SurveyObservation, OM_Output$OM_Survey)
SurveySD <- c(SurveySD, OM_Output$OM_surveyStdDev)
# StoreHabitatAvailability <- rbind(StoreHabitatAvailability, OM_Output$HabitatAvailable) #??? update after ICES
PopLat <- OM_Output$OM_PopLat # Updated vector of latitudes for population center of gravity
##### Stock assessment model ##########################################################################################
assessOutput <- stockassess(Rec_catch = sum(RecreationCatch[nrow(RecreationCatch),]),
Rec_disc = sum(RecreationDiscard[nrow(RecreationDiscard),]),
Com_catdisc = sum(CommercialFishing[nrow(CommercialFishing),]),
Survey_obs = SurveyObservation, # Historic survey series updated by OM Don't know how/what to combine, append OM production here
Survey_SD = SurveySD, # Historic survey series updated by OM
Recruits = Recruitment) # Historic recruitment updated by OM
# Update biomass, catch, and survey settings from stock assessment model output, outputs from the end of the projection will be stored in simulation output
StoreTotalBiomass <- assessOutput$biomass # Total biomass summed across states
StoreRecruitmentPred <- assessOutput$recruitment # Predicted recruitment timeseries
StoreMeanRecruitment <- assessOutput$R_mean # Mean recruitment
StoreRecDevs <- assessOutput$R_devs # Recruitment deviation timeseries
StoreCatchPred <- assessOutput$catch_pred # Predicted catch timeseries
StoreSurvival <- assessOutput$survival # Timeseries of survival
StoreF_vals <- assessOutput$F_vals # Catch F estimate timeseries
##### Management procedure model ##########################################################################################
MngmtOutput <- mngmtprocedure(DecisionArea = DecisionArea,
QuotaMethod = QuotaMethod,
Availability = OM_Output$HabitatAvailable, # Habitat availability by state, stored in StoreHabitatAvailibility
TotalBiomass = assessOutput$biomass, # ??? need to calculate total quota as part of the management procedure model
iYear = iyear,
CatchObs = RecreationCatch,
BagSize = BagSize,
MinSize = MinSize,
SeasonLength = SeasonLength,
InputMngmtMethod = InputMngmtMethod)
# Update management settings from management procedure model output
BagSize <- MngmtOutput$BagSize
MinSize <- MngmtOutput$MinSize
SeasonLength <- MngmtOutput$SeasonLength
StoreInputSettings <- rbind(StoreInputSettings, c(BagSize, MinSize, SeasonLength)) # Matrix containing BagSize, MinSize, SeasonLength settings over time(rows)
StoreAreaQuotas <- rbind(StoreAreaQuotas, MngmtOutput$AreaQuota) # Matrix containing area quota from projection
StoreCommercialQuota <- rbind(StoreCommercialQuota, MngmtOutput$CommercialQuota) # Vector containing commercial quota from projection
# Update vector of combined commercial discards and landings, assume that entire quota is landed
CommercialFishing <- c(CommercialFishing, MngmtOutput$CommercialQuota) # do commercial discards need to be calculated & added separately or are discards included in quota ???
##### Fishermen response model ##########################################################################################
responseOutput <- fishermenresponse(GAM = FishermanGAM,
MinSize = MinSize,
BagSize = BagSize,
SeasonLength = SeasonLength,
PopLat = PopLat) # ??? this needs updating after ICES
# Update catch & discards
RecreationCatch <- rbind(RecreationCatch, responseOutput$reccatPlaceholder)
RecreationDiscard <- rbind(RecreationDiscard, responseOutput$recdiscPlaceholder)
} # End of loop over projection years
##### Save simulation settings and outputs ##########################################################################################
OutputList <- list()
OutputJSON <- toJSON(OutputList)
# write(prettify(SimulationOutput), file = SimulationOutput) # ??? do I need prettify?
write(SimulationOutput, file = Simulationoutput)
} # End of function
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