Harper et al. PNAS resubmission release updates

SPATIAL-Lab · May 27, 2024 · 4695234 · 4695234
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diff --git a/.gitignore b/.gitignore
@@ -3,14 +3,13 @@
 .Rapp.history
 
 # Session Data files
-Harperetal_subm/out
+
 
 # User-specific files
-Harperetal_subm/out/inversion_sum.csv
-Harperetal_subm/RevisionApril2024/out_senstest
-Harperetal_subm/RevisionApril2024/out_primary/LPEE_prim.rda
-Harperetal_subm/RevisionApril2024/.DS_Store
-Harperetal_subm/RevisionApril2024/data/.DS_Store
+Harperetal_resubm/out_senstest
+Harperetal_resubm/out_primary/LPEE_prim.rda
+Harperetal_resubm/R.DS_Store
+Harperetal_resubm/data/.DS_Store
 
 # Example code in package build process
 *-Ex.R

diff --git a/...evisionApril2024/code/1_ForamDriverLPEE.R → Harperetal_resubm/code/1_ForamDriverLPEE.R b/...evisionApril2024/code/1_ForamDriverLPEE.R → Harperetal_resubm/code/1_ForamDriverLPEE.R
@@ -78,7 +78,7 @@ parms <- c("sal", "tempC", "press", "xca", "xmg", "xso4", "d11Bsw", "d18Osw", "p
            "m.1", "m.2", "c.1", "c.2", "alpha", "d11Bf.1", "d11Bf.2", "d18Of", "mgcaf")
 
 # Read in proxy time series data
-prox.in <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/ShatskyLPEE_data.rds")
+prox.in <- readRDS(file = "Harperetal_resubm/data/ShatskyLPEE_data.rds")
 
 # Setup age range and bins 
 ages.prox <- unique(round(prox.in$age))
@@ -155,7 +155,7 @@ pH.u = 7.75
 ############################################################################################
 # Read in D[CO3=] from file and linearly interpolate for ages associated with each time step 
 
-Dco3_in <- as.data.frame(readRDS(file = "Harperetal_subm/RevisionApril2024/data/Dco3_bw.rds"))
+Dco3_in <- as.data.frame(readRDS(file = "Harperetal_resubm/data/Dco3_bw.rds"))
 Dco3.interp <- approx(Dco3_in$age, Dco3_in$Dco3, xout=ages.prox, method="linear") 
 Dco3_2s.interp <- approx(Dco3_in$age, Dco3_in$Dco3_2s, xout=ages.prox, method="linear") 
 Dco3.avg.pri <- Dco3.interp[["y"]]
@@ -175,15 +175,15 @@ for (i in 1:length(Dco3.avg.pri)){
 ############################################################################################
 # Read in DIC from LOSCAR simulation output: mean of 2 sims for PETM and 2 sims for ETM-2 with ZT19 long-term carb chem
 
-dic_LOSCAR <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/dic_LOSCAR.rds")
+dic_LOSCAR <- readRDS(file = "Harperetal_resubm/data/dic_LOSCAR.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.LOSCAR <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.y, xout=ages.prox, method="linear") 
 dic.interp.LOSCAR.err <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.2s, xout=ages.prox, method="linear") 
 dic.LOSCAR <- dic.interp.LOSCAR[["y"]]
 dic.LOSCAR.err <- dic.interp.LOSCAR.err[["y"]] / 2
 
 # Read in DIC time series from Haynes and Hönisch (2020)
-dic_HH <- readRDS("Harperetal_subm/RevisionApril2024/data/dic_HH.rds")
+dic_HH <- readRDS("Harperetal_resubm/data/dic_HH.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.HH <- approx(dic_HH$dic.HH.x, dic_HH$dic.HH.y, xout=ages.prox, method="linear") 
 dic.HH.meanerr <- rowMeans(cbind(dic_HH$dic.HH.2sp, dic_HH$dic.HH.2sn))
@@ -289,7 +289,7 @@ data <- list("d11Bf.data1" = clean.d11B1$d11B,
 ############################################################################################
 # Run the inversion
 
-system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril2024/code/_ForamPSMLPEE.R", 
+system.time({jout = jags.parallel(model.file = "Harperetal_resubm/code/_ForamPSMLPEE.R", 
                      parameters.to.save = parms, data = data, inits = NULL, 
                      n.chains = 9, n.iter = 800000, n.burnin = 500000, n.thin = 200)})
 # 500k burn in, 9 chains, 800k iterations takes 10 hours
@@ -299,10 +299,10 @@ system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril202
 # Display summary statistics and save summary as .csv
 
 #View(jout$BUGSoutput$summary)
-write.csv(jout$BUGSoutput$summary, "Harperetal_subm/RevisionApril2024/out_primary/inversion_sum.csv")
+write.csv(jout$BUGSoutput$summary, "Harperetal_resubm/out_primary/inversion_sum.csv")
 
-save(ages.prox, file = "Harperetal_subm/RevisionApril2024/out_primary/ages.prox.rda")
-#save(jout, file = "Harperetal_subm/RevisionApril2024/out_primary/LPEE_prim.rda")
+save(ages.prox, file = "Harperetal_resubm/out_primary/ages.prox.rda")
+#save(jout, file = "Harperetal_resubm/out_primary/LPEE_prim.rda")
 
 ############################################################################################
 

diff --git a/...subm/RevisionApril2024/code/2_PlotsLPEE.R → Harperetal_resubm/code/2_PlotsLPEE.R b/...subm/RevisionApril2024/code/2_PlotsLPEE.R → Harperetal_resubm/code/2_PlotsLPEE.R
@@ -1,8 +1,10 @@
 #
 # This contains the R code to plot Figures 2, 3, and S5 in Harper et al., submission to PNAS 
-# 
+# Note that full inversion output files used here ('jout') are too big to include in the repository. 
+# Thus inversions would need to be re-run using "...Driver..." .R files. 
 
 jout <- jout_prim
+load(file = "Harperetal_resubm/out_primary/ages.prox.rda")
 
 ###########################################################################################
 # Temp, pCO2, and pH time series draw plots
@@ -117,7 +119,7 @@ print(c(mean(-LP.Dpco2), quantile(-LP.Dpco2, 0.025), quantile(-LP.Dpco2, 0.975))
 ###########################################################################################
 # LOSCAR Supplemental Figure - 5800 Gt release of -35 per mille C
 ###########################################################################################
-LOSCAR <- readRDS("Harperetal_subm/RevisionApril2024/data/LOSCAR_PETM5800.rds")
+LOSCAR <- readRDS("Harperetal_resubm/data/LOSCAR_PETM5800.rds")
 
 # read in and format PSM output 
 jout_summ <- jout$BUGSoutput$summary

diff --git a/...ril2024/code/1b3_ForamDriverLPEE_pHMgCa.R → ..._resubm/code/3a_ForamDriverLPEE_pHMgCa3.R b/...ril2024/code/1b3_ForamDriverLPEE_pHMgCa.R → ..._resubm/code/3a_ForamDriverLPEE_pHMgCa3.R
@@ -78,7 +78,7 @@ parms <- c("sal", "tempC", "press", "xca", "xmg", "xso4", "d11Bsw", "d18Osw", "p
            "m.1", "m.2", "c.1", "c.2", "alpha", "d11Bf.1", "d11Bf.2", "d18Of", "mgcaf")
 
 # Read in proxy time series data
-prox.in <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/ShatskyLPEE_data.rds")
+prox.in <- readRDS(file = "Harperetal_resubm/data/ShatskyLPEE_data.rds")
 
 # Setup age range and bins 
 ages.prox <- unique(round(prox.in$age))
@@ -155,7 +155,7 @@ pH.u = 7.75
 ############################################################################################
 # Read in D[CO3=] from file and linearly interpolate for ages associated with each time step 
 
-Dco3_in <- as.data.frame(readRDS(file = "Harperetal_subm/RevisionApril2024/data/Dco3_bw.rds"))
+Dco3_in <- as.data.frame(readRDS(file = "Harperetal_resubm/data/Dco3_bw.rds"))
 Dco3.interp <- approx(Dco3_in$age, Dco3_in$Dco3, xout=ages.prox, method="linear") 
 Dco3_2s.interp <- approx(Dco3_in$age, Dco3_in$Dco3_2s, xout=ages.prox, method="linear") 
 Dco3.avg.pri <- Dco3.interp[["y"]]
@@ -175,15 +175,15 @@ for (i in 1:length(Dco3.avg.pri)){
 ############################################################################################
 # Read in DIC from LOSCAR simulation output: mean of 2 sims for PETM and 2 sims for ETM-2 with ZT19 long-term carb chem
 
-dic_LOSCAR <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/dic_LOSCAR.rds")
+dic_LOSCAR <- readRDS(file = "Harperetal_resubm/data/dic_LOSCAR.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.LOSCAR <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.y, xout=ages.prox, method="linear") 
 dic.interp.LOSCAR.err <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.2s, xout=ages.prox, method="linear") 
 dic.LOSCAR <- dic.interp.LOSCAR[["y"]]
 dic.LOSCAR.err <- dic.interp.LOSCAR.err[["y"]] / 2
 
 # Read in DIC time series from Haynes and Hönisch (2020)
-dic_HH <- readRDS("Harperetal_subm/RevisionApril2024/data/dic_HH.rds")
+dic_HH <- readRDS("Harperetal_resubm/data/dic_HH.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.HH <- approx(dic_HH$dic.HH.x, dic_HH$dic.HH.y, xout=ages.prox, method="linear") 
 dic.HH.meanerr <- rowMeans(cbind(dic_HH$dic.HH.2sp, dic_HH$dic.HH.2sn))
@@ -289,7 +289,7 @@ data <- list("d11Bf.data1" = clean.d11B1$d11B,
 ############################################################################################
 # Run the inversion
 
-system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril2024/code/_ForamPSMLPEE.R", 
+system.time({jout = jags.parallel(model.file = "Harperetal_resubm/code/_ForamPSMLPEE.R", 
                      parameters.to.save = parms, data = data, inits = NULL, 
                      n.chains = 9, n.iter = 800000, n.burnin = 500000, n.thin = 200)})
 # 500k burn in, 9 chains, 800k iterations takes 10 hours
@@ -299,8 +299,8 @@ system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril202
 # Display summary statistics and save summary as .csv
 
 #View(jout$BUGSoutput$summary)
-#write.csv(jout$BUGSoutput$summary, "Harperetal_subm/RevisionApril2024/out/inversion_sum.csv")
+#write.csv(jout$BUGSoutput$summary, "Harperetal_resubm/out_senstest/inversion_sum.csv")
 
 ############################################################################################
-save(jout, file = "Harperetal_subm/RevisionApril2024/out/LPEE_pHMgCa3.rda")
+save(jout, file = "Harperetal_resubm/out_senstest/LPEE_pHMgCa3.rda")
 
diff --git a/...ril2024/code/1b7_ForamDriverLPEE_pHMgCa.R → ..._resubm/code/3b_ForamDriverLPEE_pHMgCa7.R b/...ril2024/code/1b7_ForamDriverLPEE_pHMgCa.R → ..._resubm/code/3b_ForamDriverLPEE_pHMgCa7.R
@@ -78,7 +78,7 @@ parms <- c("sal", "tempC", "press", "xca", "xmg", "xso4", "d11Bsw", "d18Osw", "p
            "m.1", "m.2", "c.1", "c.2", "alpha", "d11Bf.1", "d11Bf.2", "d18Of", "mgcaf")
 
 # Read in proxy time series data
-prox.in <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/ShatskyLPEE_data.rds")
+prox.in <- readRDS(file = "Harperetal_resubm/data/ShatskyLPEE_data.rds")
 
 # Setup age range and bins 
 ages.prox <- unique(round(prox.in$age))
@@ -155,7 +155,7 @@ pH.u = 7.75
 ############################################################################################
 # Read in D[CO3=] from file and linearly interpolate for ages associated with each time step 
 
-Dco3_in <- as.data.frame(readRDS(file = "Harperetal_subm/RevisionApril2024/data/Dco3_bw.rds"))
+Dco3_in <- as.data.frame(readRDS(file = "Harperetal_resubm/data/Dco3_bw.rds"))
 Dco3.interp <- approx(Dco3_in$age, Dco3_in$Dco3, xout=ages.prox, method="linear") 
 Dco3_2s.interp <- approx(Dco3_in$age, Dco3_in$Dco3_2s, xout=ages.prox, method="linear") 
 Dco3.avg.pri <- Dco3.interp[["y"]]
@@ -175,15 +175,15 @@ for (i in 1:length(Dco3.avg.pri)){
 ############################################################################################
 # Read in DIC from LOSCAR simulation output: mean of 2 sims for PETM and 2 sims for ETM-2 with ZT19 long-term carb chem
 
-dic_LOSCAR <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/dic_LOSCAR.rds")
+dic_LOSCAR <- readRDS(file = "Harperetal_resubm/data/dic_LOSCAR.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.LOSCAR <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.y, xout=ages.prox, method="linear") 
 dic.interp.LOSCAR.err <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.2s, xout=ages.prox, method="linear") 
 dic.LOSCAR <- dic.interp.LOSCAR[["y"]]
 dic.LOSCAR.err <- dic.interp.LOSCAR.err[["y"]] / 2
 
 # Read in DIC time series from Haynes and Hönisch (2020)
-dic_HH <- readRDS("Harperetal_subm/RevisionApril2024/data/dic_HH.rds")
+dic_HH <- readRDS("Harperetal_resubm/data/dic_HH.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.HH <- approx(dic_HH$dic.HH.x, dic_HH$dic.HH.y, xout=ages.prox, method="linear") 
 dic.HH.meanerr <- rowMeans(cbind(dic_HH$dic.HH.2sp, dic_HH$dic.HH.2sn))
@@ -289,7 +289,7 @@ data <- list("d11Bf.data1" = clean.d11B1$d11B,
 ############################################################################################
 # Run the inversion
 
-system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril2024/code/_ForamPSMLPEE.R", 
+system.time({jout = jags.parallel(model.file = "Harperetal_resubm/code/_ForamPSMLPEE.R", 
                      parameters.to.save = parms, data = data, inits = NULL, 
                      n.chains = 9, n.iter = 800000, n.burnin = 500000, n.thin = 200)})
 # 500k burn in, 9 chains, 800k iterations takes 10 hours
@@ -299,8 +299,8 @@ system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril202
 # Display summary statistics and save summary as .csv
 
 #View(jout$BUGSoutput$summary)
-#write.csv(jout$BUGSoutput$summary, "Harperetal_subm/RevisionApril2024/out/inversion_sum.csv")
+#write.csv(jout$BUGSoutput$summary, "Harperetal_resubm/out_senstest/inversion_sum.csv")
 
 ############################################################################################
-save(jout, file = "Harperetal_subm/RevisionApril2024/out/LPEE_pHMgCa7.rda")
+save(jout, file = "Harperetal_resubm/out_senstest/LPEE_pHMgCa7.rda")
 
diff --git a/...il2024/code/1c_ForamDriverLPEE_nopHd18O.R → ...resubm/code/3c_ForamDriverLPEE_nopHd18O.R b/...il2024/code/1c_ForamDriverLPEE_nopHd18O.R → ...resubm/code/3c_ForamDriverLPEE_nopHd18O.R
@@ -78,7 +78,7 @@ parms <- c("sal", "tempC", "press", "xca", "xmg", "xso4", "d11Bsw", "d18Osw", "p
            "m.1", "m.2", "c.1", "c.2", "alpha", "d11Bf.1", "d11Bf.2", "d18Of", "mgcaf")
 
 # Read in proxy time series data
-prox.in <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/ShatskyLPEE_data.rds")
+prox.in <- readRDS(file = "Harperetal_resubm/data/ShatskyLPEE_data.rds")
 
 # Setup age range and bins 
 ages.prox <- unique(round(prox.in$age))
@@ -155,7 +155,7 @@ pH.u = 7.75
 ############################################################################################
 # Read in D[CO3=] from file and linearly interpolate for ages associated with each time step 
 
-Dco3_in <- as.data.frame(readRDS(file = "Harperetal_subm/RevisionApril2024/data/Dco3_bw.rds"))
+Dco3_in <- as.data.frame(readRDS(file = "Harperetal_resubm/data/Dco3_bw.rds"))
 Dco3.interp <- approx(Dco3_in$age, Dco3_in$Dco3, xout=ages.prox, method="linear") 
 Dco3_2s.interp <- approx(Dco3_in$age, Dco3_in$Dco3_2s, xout=ages.prox, method="linear") 
 Dco3.avg.pri <- Dco3.interp[["y"]]
@@ -175,15 +175,15 @@ for (i in 1:length(Dco3.avg.pri)){
 ############################################################################################
 # Read in DIC from LOSCAR simulation output: mean of 2 sims for PETM and 2 sims for ETM-2 with ZT19 long-term carb chem
 
-dic_LOSCAR <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/dic_LOSCAR.rds")
+dic_LOSCAR <- readRDS(file = "Harperetal_resubm/data/dic_LOSCAR.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.LOSCAR <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.y, xout=ages.prox, method="linear") 
 dic.interp.LOSCAR.err <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.2s, xout=ages.prox, method="linear") 
 dic.LOSCAR <- dic.interp.LOSCAR[["y"]]
 dic.LOSCAR.err <- dic.interp.LOSCAR.err[["y"]] / 2
 
 # Read in DIC time series from Haynes and Hönisch (2020)
-dic_HH <- readRDS("Harperetal_subm/RevisionApril2024/data/dic_HH.rds")
+dic_HH <- readRDS("Harperetal_resubm/data/dic_HH.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.HH <- approx(dic_HH$dic.HH.x, dic_HH$dic.HH.y, xout=ages.prox, method="linear") 
 dic.HH.meanerr <- rowMeans(cbind(dic_HH$dic.HH.2sp, dic_HH$dic.HH.2sn))
@@ -289,7 +289,7 @@ data <- list("d11Bf.data1" = clean.d11B1$d11B,
 ############################################################################################
 # Run the inversion
 
-system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril2024/code/_ForamPSMLPEE.R", 
+system.time({jout = jags.parallel(model.file = "Harperetal_resubm/code/_ForamPSMLPEE.R", 
                      parameters.to.save = parms, data = data, inits = NULL, 
                      n.chains = 9, n.iter = 800000, n.burnin = 500000, n.thin = 200)})
 # 500k burn in, 9 chains, 800k iterations takes 10 hours
@@ -299,8 +299,8 @@ system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril202
 # Display summary statistics and save summary as .csv
 
 #View(jout$BUGSoutput$summary)
-#write.csv(jout$BUGSoutput$summary, "Harperetal_subm/RevisionApril2024/out/inversion_sum.csv")
+#write.csv(jout$BUGSoutput$summary, "Harperetal_resubm/out_senstest/inversion_sum.csv")
 
 ############################################################################################
-save(jout, file = "Harperetal_subm/RevisionApril2024/out/LPEE_nopHd18O.rda")
+save(jout, file = "Harperetal_resubm/out_senstest/LPEE_nopHd18O.rda")
 
diff --git a/...pril2024/code/1d_ForamDriverLPEE_MgCasw.R → ...l_resubm/code/3d_ForamDriverLPEE_MgCasw.R b/...pril2024/code/1d_ForamDriverLPEE_MgCasw.R → ...l_resubm/code/3d_ForamDriverLPEE_MgCasw.R
@@ -78,7 +78,7 @@ parms <- c("sal", "tempC", "press", "xca", "xmg", "xso4", "d11Bsw", "d18Osw", "p
            "m.1", "m.2", "c.1", "c.2", "alpha", "d11Bf.1", "d11Bf.2", "d18Of", "mgcaf")
 
 # Read in proxy time series data
-prox.in <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/ShatskyLPEE_data.rds")
+prox.in <- readRDS(file = "Harperetal_resubm/data/ShatskyLPEE_data.rds")
 
 # Setup age range and bins 
 ages.prox <- unique(round(prox.in$age))
@@ -155,7 +155,7 @@ pH.u = 7.75
 ############################################################################################
 # Read in D[CO3=] from file and linearly interpolate for ages associated with each time step 
 
-Dco3_in <- as.data.frame(readRDS(file = "Harperetal_subm/RevisionApril2024/data/Dco3_bw.rds"))
+Dco3_in <- as.data.frame(readRDS(file = "Harperetal_resubm/data/Dco3_bw.rds"))
 Dco3.interp <- approx(Dco3_in$age, Dco3_in$Dco3, xout=ages.prox, method="linear") 
 Dco3_2s.interp <- approx(Dco3_in$age, Dco3_in$Dco3_2s, xout=ages.prox, method="linear") 
 Dco3.avg.pri <- Dco3.interp[["y"]]
@@ -175,15 +175,15 @@ for (i in 1:length(Dco3.avg.pri)){
 ############################################################################################
 # Read in DIC from LOSCAR simulation output: mean of 2 sims for PETM and 2 sims for ETM-2 with ZT19 long-term carb chem
 
-dic_LOSCAR <- readRDS(file = "Harperetal_subm/RevisionApril2024/data/dic_LOSCAR.rds")
+dic_LOSCAR <- readRDS(file = "Harperetal_resubm/data/dic_LOSCAR.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.LOSCAR <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.y, xout=ages.prox, method="linear") 
 dic.interp.LOSCAR.err <- approx(dic_LOSCAR$dic.LOSCAR.x, dic_LOSCAR$dic.LOSCAR.2s, xout=ages.prox, method="linear") 
 dic.LOSCAR <- dic.interp.LOSCAR[["y"]]
 dic.LOSCAR.err <- dic.interp.LOSCAR.err[["y"]] / 2
 
 # Read in DIC time series from Haynes and Hönisch (2020)
-dic_HH <- readRDS("Harperetal_subm/RevisionApril2024/data/dic_HH.rds")
+dic_HH <- readRDS("Harperetal_resubm/data/dic_HH.rds")
 # Linearly interpolate DIC for ages associated with each time step using input DIC time series 
 dic.interp.HH <- approx(dic_HH$dic.HH.x, dic_HH$dic.HH.y, xout=ages.prox, method="linear") 
 dic.HH.meanerr <- rowMeans(cbind(dic_HH$dic.HH.2sp, dic_HH$dic.HH.2sn))
@@ -289,7 +289,7 @@ data <- list("d11Bf.data1" = clean.d11B1$d11B,
 ############################################################################################
 # Run the inversion
 
-system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril2024/code/_ForamPSMLPEE_MgCasw.R", 
+system.time({jout = jags.parallel(model.file = "Harperetal_resubm/code/_ForamPSMLPEE_MgCasw.R", 
                      parameters.to.save = parms, data = data, inits = NULL, 
                      n.chains = 9, n.iter = 800000, n.burnin = 500000, n.thin = 200)})
 # 500k burn in, 9 chains, 800k iterations takes 10 hours
@@ -299,8 +299,8 @@ system.time({jout = jags.parallel(model.file = "Harperetal_subm/RevisionApril202
 # Display summary statistics and save summary as .csv
 
 #View(jout$BUGSoutput$summary)
-#write.csv(jout$BUGSoutput$summary, "Harperetal_subm/RevisionApril2024/out/inversion_sum.csv")
+#write.csv(jout$BUGSoutput$summary, "Harperetal_resubm/out_senstest/inversion_sum.csv")
 
 ############################################################################################
-save(jout, file = "Harperetal_subm/RevisionApril2024/out/LPEE_MgCasw.rda")
+save(jout, file = "Harperetal_resubm/out_senstest/LPEE_MgCasw.rda")