Added scripts for sensitivity analysis
This commit is contained in:
Alex Gebben Work
parent
e1fc4206b4
commit
56bb7e7191
@ -108,7 +108,7 @@ SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL
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NCORES <- detectCores()-1
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BATCH_SIZE <- NCORES*10
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TOTAL_SIMULATIONS <- 10^6
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TOTAL_SIMULATIONS <- 10^5
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N_RUNS <-ceiling(TOTAL_SIMULATIONS/BATCH_SIZE )
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SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2016_Simulation.csv")
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160
1C_Run_Simulation_Upper_Bound.r
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160
1C_Run_Simulation_Upper_Bound.r
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#####Packages
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library(tidyverse) #Cleaning data
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library(fixest) #Estimating a model of birth rates, to provide variance in the birth rate Monte Carlo using a fixed effect model.
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library(forecast) #Fore ARIMA migration simulations
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library(parallel)
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library(uuid) #To add a index to each batch
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####If the prelimnary data needs to be reloaded run the supplied bash script to download, process, and generate all needed data sets for the Monte Carlo population Simulation. Otherwise skip this step to save time
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RELOAD_DATA <- FALSE
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if(RELOAD_DATA){system("bash Prelim_Process.sh")}
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#Load custom functions needed for the simulation
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source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Increment_Data_Year.r")
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source("Scripts/Load_Custom_Functions/Single_Age_Mortality_Trend_Simulation.r")
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source("Scripts/Load_Custom_Functions/Induced_Migration_Functions.r")
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#######Preliminary Model Inputs
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YEARS_AHEAD <- 41
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ST_YEAR <- 2025
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################################Load Data
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DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
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BIRTH_MOD <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Birth_Regression.Rds")
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#Must add region as a factor with multiple levels for predict to work. Seems to check for multiple levels although that is not needed econometrics.
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BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(KEM==1,Year==2023) %>% mutate(Year=2024)
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MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")
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MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
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####
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OPERATORS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Operating_Worker_Related_Migration.Rds")
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CONSTRUCTION <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Construction_Related_Migration.Rds")
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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INDUCED_MIGRATION_MULTIPLIERS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Induced_Jobs.Rds")
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##############
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#Data for death rate trends
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SINGLE_AGE_MODS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Time_Series_Regression.Rds")
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BOUNDS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Bounds_for_Predictions.Rds")
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MAX_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_RATE)
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MIN_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_RATE)
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MAX_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MAX_RATE)
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MIN_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MIN_RATE)
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MIN_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_MALE_FEMALE_GAP)
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MAX_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_MALE_FEMALE_GAP)
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BASELINE_AGE_ADJUST_MEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Male') %>% pull(Percent_of_Population)
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BASELINE_AGE_ADJUST_WOMEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Female') %>% pull(Percent_of_Population)
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#Adjust to just women popualtion (Not all population percent
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BASELINE_AGE_ADJUST_WOMEN <- BASELINE_AGE_ADJUST_WOMEN/sum(BASELINE_AGE_ADJUST_WOMEN )
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BASELINE_AGE_ADJUST_MEN <- BASELINE_AGE_ADJUST_MEN/ sum(BASELINE_AGE_ADJUST_MEN )
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MOD_MEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Men_Mortality_by_Age.Rds")
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MOD_WOMEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Women_Mortality_by_Age.Rds")
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MOD_LIN_MEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Men_Mortality_by_Age.Rds")
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MOD_LIN_WOMEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Women_Mortality_by_Age.Rds")
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XREG <- cbind(rep(0.0001,YEARS_AHEAD+1),rep(0.0001,YEARS_AHEAD+1)) #Empty data set to simulate in the future
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XREG <- ts(XREG,start=ST_YEAR,frequency=1)
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SIMULATE_MORTALITY_RATE_TRENDS <- function(){
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SIMULATED_MORTALITY_DATA_SET <- MAKE_EMPTY(ST_YEAR,ST_YEAR+YEARS_AHEAD,MOD_LIN_MEN,MOD_LIN_WOMEN,MOD_MEN_ALL,MOD_WOMEN_ALL,XREG)
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MORTALITY_SIMULATION <- AGE_DIST(SINGLE_AGE_MODS,SIMULATED_MORTALITY_DATA_SET ,MAX_MALE,MAX_FEMALE,MIN_MALE,MIN_FEMALE,MAX_GAP,MIN_GAP,BASELINE_AGE_ADJUST_MEN,BASELINE_AGE_ADJUST_WOMEN)
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return(MORTALITY_SIMULATION )
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}
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#####################START YEAR BY SIMULATIONS
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#CURRENT_YEARS_AHEAD=1;CURRENT_SIM_NUM <- 1;MORTALITY_SIMULATION <- SIMULATE_MORTALITY_RATE_TRENDS()
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SINGLE_YEAR_SIM <- function(DEMO,BIRTH_DATA,CURRENT_YEARS_AHEAD,MORTALITY_SIMULATION,NET_MIGRATION){
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ORIG_DEMO <- DEMO
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DEMO <- DEMOGRAPHICS_AFTER_MIGRATION(DEMO, NET_MIGRATION,MIGRATION_ODDS )
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TOTAL_MIGRATION <- sum(DEMO-ORIG_DEMO)
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BIRTH_DATA$Year <- BIRTH_DATA$Year+1
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BIRTH_DATA$Lag_Two_Births <- BIRTH_DATA$Lag_Births
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BIRTH_DATA$Lag_Births <- BIRTH_DATA$Births
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BIRTH_DATA$Births <- NA
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##We grab one year earlier than the window because they are one year older this year. Because the ages are from 0-85, row 18 is year 17, but one year is added making it 18 years in the current year. The birth windows are 18-28 for women and 18-30 for men.
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BIRTH_DATA$Min_Birth_Group <- min(sum(DEMO[18:30,1]),sum(DEMO[18:28,2]))
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NEW_BORNS <- BIRTH_SIM(BIRTH_MOD,BIRTH_DATA)
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TOTAL_BIRTHS <- sum(NEW_BORNS)
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BIRTH_DATA[,"Births"] <- TOTAL_BIRTHS
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DEMO <- INCREMENT_AGES(DEMO,NEW_BORNS)
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MORTALITY_SIMULATION
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MALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,1],MORTALITY_SIMULATION[[1]][x,CURRENT_YEARS_AHEAD])})
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FEMALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,2],MORTALITY_SIMULATION[[2]][x,CURRENT_YEARS_AHEAD])})
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MALE_DEATHS <- ifelse(MALE_DEATHS>=DEMO[,1],DEMO[,1],MALE_DEATHS)
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FEMALE_DEATHS <- ifelse(FEMALE_DEATHS>=DEMO[,1],DEMO[,1],FEMALE_DEATHS)
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TOTAL_DEATHS <- sum(MALE_DEATHS+FEMALE_DEATHS)
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DEMO[,"Num_Male"] <- DEMO[,"Num_Male"] -MALE_DEATHS
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DEMO[,"Num_Female"] <- DEMO[,"Num_Female"] -FEMALE_DEATHS
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#List of values needed for the next run or for reporting a result
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TOTAL_POP <- sum(DEMO)
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return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATION)))
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}
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MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
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#DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS
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CONSTRUCTION_MIGRATION <- CONSTRUCTION_LIN_MIGRATION
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MIGRATION_MULTIPLIERS <- INDUCED_MIGRATION_MULTIPLIERS
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OPERATOR_MIGRATION <- OPERATOR_LIN_MIGRATION
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SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_MIGRATION,CONSTRUCTION_MIGRATION,MIGRATION_MULTIPLIERS ){
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TERRA_POWER_EFFECT <- rep(0,YEARS_AHEAD)
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OPERATOR_MIGRATION <- LOCAL_WORK_ADJ(OPERATOR_MIGRATION ,0.85) #Assume between 85%-100% operators live in Kemmerer
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CONSTRUCTION_MIGRATION <- LOCAL_WORK_ADJ(CONSTRUCTION_MIGRATION,0.41) #Assume between 41%-100% operators live in Kemmerer
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CONSTRUCTION_MIGRATION[7] <- CONSTRUCTION_MIGRATION[7] - sum(CONSTRUCTION_MIGRATION )
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CONSTRUCTION_POPULATION_ADDED <- cumsum(CONSTRUCTION_MIGRATION)
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PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,MIGRATION_MULTIPLIERS)+OPERATOR_MIGRATION
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###############NOTE NEED TO USE THIS AT END TO ADJUST THE RESULTS WHILE LEAVING THE DEMOGRAPHIC MATRIX
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TERRA_POWER_EFFECT[1:7] <- TERRA_POWER_EFFECT[1:7]+CONSTRUCTION_MIGRATION
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MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))+TERRA_POWER_EFFECT)
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#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
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FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)
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colnames(FINAL_REPORT_VALUES ) <- c("Sim_UUID","Year","Population","Births","Deaths","Net_Migration")
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FINAL_REPORT_VALUES[,1] <- UUIDgenerate()
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for(i in 1:YEARS_AHEAD){
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C_YEAR <- ST_YEAR+i-1
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C_RES <-SINGLE_YEAR_SIM(DEMO,BIRTH_DATA,i,SIMULATE_MORTALITY_RATE_TRENDS(),MIGRATION_SIM_VALUES[i])
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DEMO <- C_RES[[1]]
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BIRTH_DATA <- C_RES[[2]]
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FINAL_REPORT_VALUES[i,-1] <- c(C_YEAR,C_RES[[3]])
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}
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FINAL_REPORT_VALUES[1:7,3] <- as.numeric(FINAL_REPORT_VALUES[1:7,3])+CONSTRUCTION_POPULATION_ADDED
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FINAL_REPORT_VALUES[1:7,6] <- as.numeric(FINAL_REPORT_VALUES[1:7,6]) +CONSTRUCTION_MIGRATION
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return(FINAL_REPORT_VALUES)
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}
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if(!exists("RES_DIR")){RES_DIR<- "./Results/"}
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if(!exists("RES_SIM_DIR")){RES_SIM_DIR <- paste0(RES_DIR,"Simulations/")}
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dir.create(RES_SIM_DIR, recursive = TRUE, showWarnings = FALSE)
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NCORES <- detectCores()-1
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BATCH_SIZE <- NCORES*10
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TOTAL_SIMULATIONS <- 10^5
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N_RUNS <-ceiling(TOTAL_SIMULATIONS/BATCH_SIZE )
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SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2024_Simulation_County_Migration_Rate.csv")
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NEW_RES_FILE <- !file.exists(SIM_RES_FILE)
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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for(i in 1:N_RUNS){
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# MIGRATION_MATRIX <- simulate(nsim=BATCH_SIZE,MIGRATION_ARIMA,n=YEARS_AHEAD)
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MIGRATION_MATRIX <- do.call(cbind, mclapply(1:BATCH_SIZE,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
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# rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
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# colnames(MIGRATION_MATRIX) <- 1:BATCH_SIZE
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#SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)
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try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)},mc.cores=NCORES)))
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if(exists("RES")){
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RES <- as.data.frame(RES)
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RES[,-1] <- as.numeric(as.matrix(RES[,-1]))
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if(NEW_RES_FILE & i==1){write_csv(RES,SIM_RES_FILE)}else {write_csv(RES,SIM_RES_FILE,col_names=FALSE,append=TRUE)}
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rm(RES)
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}
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}
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160
1D_Run_Simulation_Lower_Bound.r
Normal file
160
1D_Run_Simulation_Lower_Bound.r
Normal file
@ -0,0 +1,160 @@
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#####Packages
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library(tidyverse) #Cleaning data
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library(fixest) #Estimating a model of birth rates, to provide variance in the birth rate Monte Carlo using a fixed effect model.
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library(forecast) #Fore ARIMA migration simulations
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library(parallel)
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library(uuid) #To add a index to each batch
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####If the prelimnary data needs to be reloaded run the supplied bash script to download, process, and generate all needed data sets for the Monte Carlo population Simulation. Otherwise skip this step to save time
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RELOAD_DATA <- FALSE
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if(RELOAD_DATA){system("bash Prelim_Process.sh")}
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#Load custom functions needed for the simulation
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source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Increment_Data_Year.r")
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source("Scripts/Load_Custom_Functions/Single_Age_Mortality_Trend_Simulation.r")
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source("Scripts/Load_Custom_Functions/Induced_Migration_Functions.r")
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#######Preliminary Model Inputs
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YEARS_AHEAD <- 41
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ST_YEAR <- 2025
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################################Load Data
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DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
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BIRTH_MOD <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Birth_Regression.Rds")
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#Must add region as a factor with multiple levels for predict to work. Seems to check for multiple levels although that is not needed econometrics.
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BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(KEM==1,Year==2023) %>% mutate(Year=2024)
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MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")
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MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
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####
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OPERATORS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Operating_Worker_Related_Migration.Rds")
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CONSTRUCTION <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Construction_Related_Migration.Rds")
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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INDUCED_MIGRATION_MULTIPLIERS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Induced_Jobs.Rds")
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##############
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#Data for death rate trends
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SINGLE_AGE_MODS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Time_Series_Regression.Rds")
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BOUNDS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Bounds_for_Predictions.Rds")
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MAX_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_RATE)
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MIN_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_RATE)
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MAX_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MAX_RATE)
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MIN_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MIN_RATE)
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MIN_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_MALE_FEMALE_GAP)
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MAX_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_MALE_FEMALE_GAP)
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BASELINE_AGE_ADJUST_MEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Male') %>% pull(Percent_of_Population)
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BASELINE_AGE_ADJUST_WOMEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Female') %>% pull(Percent_of_Population)
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#Adjust to just women popualtion (Not all population percent
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BASELINE_AGE_ADJUST_WOMEN <- BASELINE_AGE_ADJUST_WOMEN/sum(BASELINE_AGE_ADJUST_WOMEN )
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BASELINE_AGE_ADJUST_MEN <- BASELINE_AGE_ADJUST_MEN/ sum(BASELINE_AGE_ADJUST_MEN )
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MOD_MEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Men_Mortality_by_Age.Rds")
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MOD_WOMEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Women_Mortality_by_Age.Rds")
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MOD_LIN_MEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Men_Mortality_by_Age.Rds")
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MOD_LIN_WOMEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Women_Mortality_by_Age.Rds")
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XREG <- cbind(rep(0.0001,YEARS_AHEAD+1),rep(0.0001,YEARS_AHEAD+1)) #Empty data set to simulate in the future
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XREG <- ts(XREG,start=ST_YEAR,frequency=1)
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SIMULATE_MORTALITY_RATE_TRENDS <- function(){
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SIMULATED_MORTALITY_DATA_SET <- MAKE_EMPTY(ST_YEAR,ST_YEAR+YEARS_AHEAD,MOD_LIN_MEN,MOD_LIN_WOMEN,MOD_MEN_ALL,MOD_WOMEN_ALL,XREG)
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MORTALITY_SIMULATION <- AGE_DIST(SINGLE_AGE_MODS,SIMULATED_MORTALITY_DATA_SET ,MAX_MALE,MAX_FEMALE,MIN_MALE,MIN_FEMALE,MAX_GAP,MIN_GAP,BASELINE_AGE_ADJUST_MEN,BASELINE_AGE_ADJUST_WOMEN)
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return(MORTALITY_SIMULATION )
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}
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#####################START YEAR BY SIMULATIONS
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#CURRENT_YEARS_AHEAD=1;CURRENT_SIM_NUM <- 1;MORTALITY_SIMULATION <- SIMULATE_MORTALITY_RATE_TRENDS()
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SINGLE_YEAR_SIM <- function(DEMO,BIRTH_DATA,CURRENT_YEARS_AHEAD,MORTALITY_SIMULATION,NET_MIGRATION){
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ORIG_DEMO <- DEMO
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DEMO <- DEMOGRAPHICS_AFTER_MIGRATION(DEMO, NET_MIGRATION,MIGRATION_ODDS )
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TOTAL_MIGRATION <- sum(DEMO-ORIG_DEMO)
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BIRTH_DATA$Year <- BIRTH_DATA$Year+1
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BIRTH_DATA$Lag_Two_Births <- BIRTH_DATA$Lag_Births
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BIRTH_DATA$Lag_Births <- BIRTH_DATA$Births
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BIRTH_DATA$Births <- NA
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##We grab one year earlier than the window because they are one year older this year. Because the ages are from 0-85, row 18 is year 17, but one year is added making it 18 years in the current year. The birth windows are 18-28 for women and 18-30 for men.
|
||||
BIRTH_DATA$Min_Birth_Group <- min(sum(DEMO[18:30,1]),sum(DEMO[18:28,2]))
|
||||
NEW_BORNS <- BIRTH_SIM(BIRTH_MOD,BIRTH_DATA)
|
||||
TOTAL_BIRTHS <- sum(NEW_BORNS)
|
||||
BIRTH_DATA[,"Births"] <- TOTAL_BIRTHS
|
||||
DEMO <- INCREMENT_AGES(DEMO,NEW_BORNS)
|
||||
MORTALITY_SIMULATION
|
||||
MALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,1],MORTALITY_SIMULATION[[1]][x,CURRENT_YEARS_AHEAD])})
|
||||
FEMALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,2],MORTALITY_SIMULATION[[2]][x,CURRENT_YEARS_AHEAD])})
|
||||
MALE_DEATHS <- ifelse(MALE_DEATHS>=DEMO[,1],DEMO[,1],MALE_DEATHS)
|
||||
FEMALE_DEATHS <- ifelse(FEMALE_DEATHS>=DEMO[,1],DEMO[,1],FEMALE_DEATHS)
|
||||
|
||||
TOTAL_DEATHS <- sum(MALE_DEATHS+FEMALE_DEATHS)
|
||||
DEMO[,"Num_Male"] <- DEMO[,"Num_Male"] -MALE_DEATHS
|
||||
DEMO[,"Num_Female"] <- DEMO[,"Num_Female"] -FEMALE_DEATHS
|
||||
#List of values needed for the next run or for reporting a result
|
||||
TOTAL_POP <- sum(DEMO)
|
||||
return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATION)))
|
||||
}
|
||||
|
||||
MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
|
||||
#DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS
|
||||
CONSTRUCTION_MIGRATION <- CONSTRUCTION_LIN_MIGRATION
|
||||
MIGRATION_MULTIPLIERS <- INDUCED_MIGRATION_MULTIPLIERS
|
||||
OPERATOR_MIGRATION <- OPERATOR_LIN_MIGRATION
|
||||
|
||||
SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_MIGRATION,CONSTRUCTION_MIGRATION,MIGRATION_MULTIPLIERS ){
|
||||
|
||||
TERRA_POWER_EFFECT <- rep(0,YEARS_AHEAD)
|
||||
OPERATOR_MIGRATION <- LOCAL_WORK_ADJ(OPERATOR_MIGRATION ,0.85) #Assume between 85%-100% operators live in Kemmerer
|
||||
CONSTRUCTION_MIGRATION <- LOCAL_WORK_ADJ(CONSTRUCTION_MIGRATION,0.41) #Assume between 41%-100% operators live in Kemmerer
|
||||
CONSTRUCTION_MIGRATION[7] <- CONSTRUCTION_MIGRATION[7] - sum(CONSTRUCTION_MIGRATION )
|
||||
CONSTRUCTION_POPULATION_ADDED <- cumsum(CONSTRUCTION_MIGRATION)
|
||||
PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,MIGRATION_MULTIPLIERS)+OPERATOR_MIGRATION
|
||||
|
||||
###############NOTE NEED TO USE THIS AT END TO ADJUST THE RESULTS WHILE LEAVING THE DEMOGRAPHIC MATRIX
|
||||
TERRA_POWER_EFFECT[1:7] <- TERRA_POWER_EFFECT[1:7]+CONSTRUCTION_MIGRATION
|
||||
MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))-55+TERRA_POWER_EFFECT)
|
||||
#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
|
||||
|
||||
FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)
|
||||
colnames(FINAL_REPORT_VALUES ) <- c("Sim_UUID","Year","Population","Births","Deaths","Net_Migration")
|
||||
FINAL_REPORT_VALUES[,1] <- UUIDgenerate()
|
||||
for(i in 1:YEARS_AHEAD){
|
||||
C_YEAR <- ST_YEAR+i-1
|
||||
C_RES <-SINGLE_YEAR_SIM(DEMO,BIRTH_DATA,i,SIMULATE_MORTALITY_RATE_TRENDS(),MIGRATION_SIM_VALUES[i])
|
||||
DEMO <- C_RES[[1]]
|
||||
BIRTH_DATA <- C_RES[[2]]
|
||||
FINAL_REPORT_VALUES[i,-1] <- c(C_YEAR,C_RES[[3]])
|
||||
}
|
||||
FINAL_REPORT_VALUES[1:7,3] <- as.numeric(FINAL_REPORT_VALUES[1:7,3])+CONSTRUCTION_POPULATION_ADDED
|
||||
FINAL_REPORT_VALUES[1:7,6] <- as.numeric(FINAL_REPORT_VALUES[1:7,6]) +CONSTRUCTION_MIGRATION
|
||||
return(FINAL_REPORT_VALUES)
|
||||
}
|
||||
|
||||
if(!exists("RES_DIR")){RES_DIR<- "./Results/"}
|
||||
if(!exists("RES_SIM_DIR")){RES_SIM_DIR <- paste0(RES_DIR,"Simulations/")}
|
||||
dir.create(RES_SIM_DIR, recursive = TRUE, showWarnings = FALSE)
|
||||
|
||||
|
||||
NCORES <- detectCores()-1
|
||||
BATCH_SIZE <- NCORES*10
|
||||
TOTAL_SIMULATIONS <- 10^5
|
||||
N_RUNS <-ceiling(TOTAL_SIMULATIONS/BATCH_SIZE )
|
||||
|
||||
SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2024_Simulation_Historic_Migration_Rate.csv")
|
||||
NEW_RES_FILE <- !file.exists(SIM_RES_FILE)
|
||||
OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
|
||||
CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
|
||||
|
||||
for(i in 1:N_RUNS){
|
||||
# MIGRATION_MATRIX <- simulate(nsim=BATCH_SIZE,MIGRATION_ARIMA,n=YEARS_AHEAD)
|
||||
MIGRATION_MATRIX <- do.call(cbind, mclapply(1:BATCH_SIZE,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
|
||||
# rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
|
||||
# colnames(MIGRATION_MATRIX) <- 1:BATCH_SIZE
|
||||
#SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)
|
||||
try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)},mc.cores=NCORES)))
|
||||
if(exists("RES")){
|
||||
RES <- as.data.frame(RES)
|
||||
RES[,-1] <- as.numeric(as.matrix(RES[,-1]))
|
||||
if(NEW_RES_FILE & i==1){write_csv(RES,SIM_RES_FILE)}else {write_csv(RES,SIM_RES_FILE,col_names=FALSE,append=TRUE)}
|
||||
rm(RES)
|
||||
}
|
||||
}
|
||||
|
||||
48
Run_Simulations.sh
Normal file
48
Run_Simulations.sh
Normal file
@ -0,0 +1,48 @@
|
||||
#Run all simulations as needed. Comment out to skip any. All but the primary results have 10^5 simulations
|
||||
echo "Starting all model Simulations. This will take a while!!"
|
||||
|
||||
#start_time=$(date +%s)
|
||||
#Rscript 1A_Run_Full_Simulation_2016.r
|
||||
#echo "Pre-2016 data simulations for benchmark complete! 100k simulations total"
|
||||
# Your command or script here
|
||||
#end_time=$(date +%s)
|
||||
#elapsed_seconds=$((end_time - start_time))
|
||||
#hours=$((elapsed_seconds / 3600))
|
||||
#minutes=$(( (elapsed_seconds % 3600) / 60 ))
|
||||
#seconds=$(( elapsed_seconds % 60 ))
|
||||
#printf "Execution time: %02d hours, %02d minutes, %02d seconds\n" "$hours" "$minutes" "$seconds"
|
||||
|
||||
|
||||
start_time=$(date +%s)
|
||||
Rscript 1C_Run_Simulation_Upper_Bound.r
|
||||
echo "Upper bound migration with county average migration rates complete! 100k simulations total"
|
||||
end_time=$(date +%s)
|
||||
elapsed_seconds=$((end_time - start_time))
|
||||
hours=$((elapsed_seconds / 3600))
|
||||
minutes=$(( (elapsed_seconds % 3600) / 60 ))
|
||||
seconds=$(( elapsed_seconds % 60 ))
|
||||
printf "Execution time: %02d hours, %02d minutes, %02d seconds\n" "$hours" "$minutes" "$seconds"
|
||||
|
||||
|
||||
start_time=$(date +%s)
|
||||
Rscript 1D_Run_Simulation_Lower_Bound.r
|
||||
echo "Lower bound migration with Kemmerere average migration rates complete! 100k simulations total"
|
||||
end_time=$(date +%s)
|
||||
elapsed_seconds=$((end_time - start_time))
|
||||
hours=$((elapsed_seconds / 3600))
|
||||
minutes=$(( (elapsed_seconds % 3600) / 60 ))
|
||||
seconds=$(( elapsed_seconds % 60 ))
|
||||
printf "Execution time: %02d hours, %02d minutes, %02d seconds\n" "$hours" "$minutes" "$seconds"
|
||||
|
||||
|
||||
|
||||
start_time=$(date +%s)
|
||||
Rscript 1B_Run_Simulation_Lower_Bound.r
|
||||
echo "Main results with variable migration rates complete! 1 million simulations total"
|
||||
end_time=$(date +%s)
|
||||
elapsed_seconds=$((end_time - start_time))
|
||||
hours=$((elapsed_seconds / 3600))
|
||||
minutes=$(( (elapsed_seconds % 3600) / 60 ))
|
||||
seconds=$(( elapsed_seconds % 60 ))
|
||||
printf "Execution time: %02d hours, %02d minutes, %02d seconds\n" "$hours" "$minutes" "$seconds"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user