Merged all changes from CMISST_orig

R/createStoredObjects.R

@@ -34,7 +34,7 @@ min.lon=0
 max.lon=360
 min.lat=-90
 max.lat=90
-years=seq(1967, 2023, 1)
+years=seq(1950, 2023, 1)
 months=seq(1,12,1)
 
 # Function defined in getOceanData.R
@@ -43,7 +43,7 @@ oceanData_ERSST<-getOceanData(dataSet=dataSet,
                         min.lat=min.lat, max.lat=max.lat,
                         years = years, months = months)
 save(x = "oceanData_ERSST", file = 'data/oceanSSTData.RData')
-load('data/oceanSSTData.RData')
+#load('data/oceanSSTData.RData')
 
 
 # SSH ---------------------------------------------------------------------
@@ -74,7 +74,7 @@ oceanData_SSH<-getOceanData(dataSet=dataSet,
                             min.lat=min.lat, max.lat=max.lat,
                             years = years, months = months)
 save(x = "oceanData_SSH", file = 'data/oceanSSHData.RData')
-load('data/oceanSSHData.RData')
+#load('data/oceanSSHData.RData')
 
 
 
@@ -95,6 +95,6 @@ response <- response[response$Year <= 2024 & response$Year >= 1950 & !is.na(resp
 names(response)[names(response) == 'Year'] <- 'year'
 
 save(x = "response", file = 'data/responseData.RData')
-load('data/oceanSSHData.RData')
+#load('data/oceanSSHData.RData')
 
 

R/crossValidation.R

@@ -57,6 +57,6 @@ LOO_CV <- function(response = response,
   se <- function(x) { return(sd(x)/sqrt(length(x))) }
   mae$season<-factor(mae$season, levels = c("win","spr","sum","aut"))
   # Trying this out - if I leave this line out, I might get all the individual predictions
-  if(exists("pred_out") & !pred_out) mae<-summaryBy(mae ~ model+season, data = mae, FUN = c(mean, se))
+  if(exists("pred_out")) if (!pred_out) mae<-summaryBy(mae ~ model+season, data = mae, FUN = c(mean, se))
   return(list(mae))
 }

R/makePlots.R

@@ -63,6 +63,7 @@ makeBiplot <- function(input.season = input.season, cmisst = cmisst) {
        x = par("usr")[1]*0.8, y=par("usr")[4]*0.80, cex=1.6, col="blue")
   if (input.loocv) {
     mae <- cmisst[[7]]
+    colnames(mae)[colnames(mae)=="mae.mean"] <- "mae"
     text(paste("MAE =", round(mean(abs(mae[mae$season==input.season,"mae"])), 2)),
          x = par("usr")[1]*0.75, y=par("usr")[4]*0.60, cex=1.6, col="blue")
   }
@@ -158,4 +159,4 @@ makeTable <- function(cmisst = cmisst) {
   out$year <- as.integer(out$year)#out <- out[,c(5,6,1:4)]
   out$response <- index$response
   out
-}
+}

R/run_CMISST.R

@@ -23,7 +23,6 @@ load("data/land.Rdata")
 load('data/responseData.RData')
 # ERSST data
 load('data/oceanSSTData.RData')
-oceanData <- oceanData_ERSST
 
 # define the updateCMISST() function
 source('R/updateCMISST.R')
@@ -41,6 +40,8 @@ months=seq(1,12,1)
 #       Define Parameters
 #************************************
 input.spatialData = "ERSST"
+if (input.spatialData == "ERSST") oceanData <- oceanData_ERSST
+if (input.spatialData == "SSH") oceanData <- oceanData_SSH
 
 # Input: Choose a stock 
 input.stock = "Sp_Chinook"
@@ -72,7 +73,7 @@ input.years= c(1980, 2023)
 #   emulating a forecasting scenario.  How many years should be included?
 #   E.g., 5 will only test the 5 most recent years, and using the full
 #   time series length will remove every data point (one at a time)
-loocvYears=5 # the most recent X years to include in the LOO CV
+loocvYears=10 # the most recent X years to include in the LOO CV
 
 # MAE LOO CV? 
 input.loocv= TRUE