Merge pull request #70 from JGCRI/RenameExpectations

Update names of expectations to match paper

DESCRIPTION

@@ -29,6 +29,7 @@ Suggests:
     rmarkdown
 BugReports: https://github.com/JGCRI/gcamland/issues
 Collate:
+    'adaptive_expectation.R'
     'ag_production_technology.R'
     'bayesian.R'
     'constants.R'
@@ -37,7 +38,6 @@ Collate:
     'generate_price_data.R'
     'generate_scenario_info.R'
     'helpers.R'
-    'lagged_expectation.R'
     'land_allocator.R'
     'land_leaf.R'
     'land_node.R'

NAMESPACE

@@ -16,7 +16,7 @@ export(SRB.SCENARIO.INFO)
 export(ScenarioInfo)
 export(add_parameter_data)
 export(as.ScenarioInfo)
-export(calc_lagged_expectation)
+export(calc_adaptive_expectation)
 export(export_results)
 export(get_PCHES_results)
 export(get_hindcast_AgProdChange)

R/lagged_expectation.R → R/adaptive_expectation.R

@@ -1,15 +1,15 @@
-# lagged_expectation.R
+# adaptive_expectation.R
 
-#' LaggedExpectation_calcExpectedYield
+#' AdaptiveExpectation_calcExpectedYield
 #'
 #' @details Calculate the expected yield for a LandLeaf using
-#'          a lagged approach -- use linear combination of previous expectation and
+#'          an adaptive approach -- use linear combination of previous expectation and
 #'          new information.
 #' @param aLandLeaf LandLeaf to calculate expected yield for
 #' @param aPeriod Current model period
 #' @param aScenarioInfo Scenario-related information, including names, logits, expectations
 #' @author KVC November 2017
-LaggedExpectation_calcExpectedYield <- function(aLandLeaf, aPeriod, aScenarioInfo) {
+AdaptiveExpectation_calcExpectedYield <- function(aLandLeaf, aPeriod, aScenarioInfo) {
   # Silence package checks
   sector <- year <- yield <- lm <- predict <- GCAM_commodity <- NULL
 
@@ -30,7 +30,7 @@ LaggedExpectation_calcExpectedYield <- function(aLandLeaf, aPeriod, aScenarioInf
     previousExpectation <- aLandLeaf$mExpectedYield[[aPeriod - 1]]
 
     # Get new information
-    if( aScenarioInfo$mExpectationType == "LaggedCurr" ) {
+    if( aScenarioInfo$mExpectationType == "HybridPerfectAdaptive" ) {
       newInformation <- aLandLeaf$mYield[[aPeriod]]
     } else {
       newInformation <- aLandLeaf$mYield[[aPeriod - 1]]
@@ -59,14 +59,14 @@ LaggedExpectation_calcExpectedYield <- function(aLandLeaf, aPeriod, aScenarioInf
     }
     yield_table$yield <- yield_table$base_yield * yield_table$yield_ratio
 
-    # Now, we call calc_lagged_expectation() to calculate the expectations
-    if( aScenarioInfo$mExpectationType == "LaggedCurr" ) {
+    # Now, we call calc_adaptive_expectation() to calculate the expectations
+    if( aScenarioInfo$mExpectationType == "HybridPerfectAdaptive" ) {
       currYear <- get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType)
-      expectedYield <- calc_lagged_expectation(currYear, shareOld, yield_table, 'yield')
+      expectedYield <- calc_adaptive_expectation(currYear, shareOld, yield_table, 'yield')
     } else {
       timestep <- get_per_to_yr(aPeriod+1, aScenarioInfo$mScenarioType) - get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType)
       prevYear <- get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType) - timestep
-      expectedYield <- calc_lagged_expectation(prevYear, shareOld, yield_table, 'yield')
+      expectedYield <- calc_adaptive_expectation(prevYear, shareOld, yield_table, 'yield')
     }
   }
 
@@ -76,16 +76,16 @@ LaggedExpectation_calcExpectedYield <- function(aLandLeaf, aPeriod, aScenarioInf
   return(expectedYield)
 }
 
-#' LaggedExpectation_calcExpectedPrice
+#' AdaptiveExpectation_calcExpectedPrice
 #'
 #' @details Calculate the expected price for a LandLeaf using
-#'          a lagged approach -- use linear combination of previous expectation and
+#'          an adaptive approach -- use linear combination of previous expectation and
 #'          new information.
 #' @param aLandLeaf LandLeaf to calculate expected price for
 #' @param aPeriod Current model period
 #' @param aScenarioInfo Scenario-related information, including names, logits, expectations
 #' @author KVC November 2017
-LaggedExpectation_calcExpectedPrice <- function(aLandLeaf, aPeriod, aScenarioInfo){
+AdaptiveExpectation_calcExpectedPrice <- function(aLandLeaf, aPeriod, aScenarioInfo){
   # Silence package checks
   sector <- lm <- predict <- year <- price <- NULL
 
@@ -106,17 +106,17 @@ LaggedExpectation_calcExpectedPrice <- function(aLandLeaf, aPeriod, aScenarioInf
 
   if(aLandLeaf$mProductName[1] %in% unique(price_table$sector)) {
     # Calculate expected price
-    if( aScenarioInfo$mExpectationType == "LaggedCurr" ) {
+    if( aScenarioInfo$mExpectationType == "HybridPerfectAdaptive" ) {
       currYear <- get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType)
-      expectedPrice <- calc_lagged_expectation(currYear, shareOld, price_table, 'price')
+      expectedPrice <- calc_adaptive_expectation(currYear, shareOld, price_table, 'price')
     } else {
       if( aPeriod > 1 ) {
         prevYear <- get_per_to_yr(aPeriod-1, aScenarioInfo$mScenarioType)
       } else {
         timestep <- get_per_to_yr(aPeriod+1, aScenarioInfo$mScenarioType) - get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType)
         prevYear <- get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType) - timestep
       }
-      expectedPrice <- calc_lagged_expectation(prevYear, shareOld, price_table, 'price')
+      expectedPrice <- calc_adaptive_expectation(prevYear, shareOld, price_table, 'price')
     }
 
   } else {
@@ -155,7 +155,7 @@ LaggedExpectation_calcExpectedPrice <- function(aLandLeaf, aPeriod, aScenarioInf
 #' @param colname Name of the column that has the data for which we are
 #' computing the expectation (e.g. \code{'price'})
 #' @export
-calc_lagged_expectation <- function(t, alpha, datatbl, colname)
+calc_adaptive_expectation <- function(t, alpha, datatbl, colname)
 {
   year <- datatbl[['year']]
   x <- datatbl[[colname]]

R/ag_production_technology.R

@@ -90,11 +90,11 @@ AgProductionTechnology_calcProfitRate <- function(aLandLeaf, aPeriod, aScenarioI
   } else if(aScenarioInfo$mExpectationType == "Linear") {
     expectedPrice <- LinearExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo)
     expectedYield <- LinearExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo)
-  } else if(aScenarioInfo$mExpectationType == "Lagged" | aScenarioInfo$mExpectationType == "LaggedCurr") {
-    expectedPrice <- LaggedExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo)
-    expectedYield <- LaggedExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo)
-  } else if(aScenarioInfo$mExpectationType == "Mixed") {
-    expectedPrice <- LaggedExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo)
+  } else if(aScenarioInfo$mExpectationType == "Adaptive" | aScenarioInfo$mExpectationType == "HybridPerfectAdaptive") {
+    expectedPrice <- AdaptiveExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo)
+    expectedYield <- AdaptiveExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo)
+  } else if(aScenarioInfo$mExpectationType == "HybridLinearAdaptive") {
+    expectedPrice <- AdaptiveExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo)
     expectedYield <- LinearExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo)
   }
 

R/generate_scenario_info.R

@@ -42,7 +42,7 @@ DEFAULT.SCENARIO.TYPE <- "Reference"
 #' @return New ScenarioInfo object
 #' @export
 #' @author KVC November 2017
-ScenarioInfo <- function(# Currently only "Perfect", "Linear", "Lagged", and "LaggedCurr" ExpectationType are supported
+ScenarioInfo <- function(# Currently only "Perfect", "Linear", "Adaptive", "HybridLinearAdaptive", and "HybridPerfectAdaptive" ExpectationType are supported
                          aExpectationType = NULL,
                          aLaggedShareOld1 = NA,
                          aLaggedShareOld2 = NA,
@@ -280,7 +280,7 @@ update_scen_info <- function(aName = NULL, aScenarioType = DEFAULT.SCENARIO.TYPE
     new_scen_info$mLinearYears3 <- aLinearYears3
   }
 
-  # Set share of old expectations in lagged expectation if specified
+  # Set share of old expectations in adaptive expectation if specified
   if(is.numeric(aLaggedShareOld)) {
     # Set all groups to this value if it specified. These can be individually overwritten later.
     new_scen_info$mLaggedShareOld1 <- aLaggedShareOld

R/helpers.R

@@ -73,7 +73,7 @@ getStartYear <- function(aScenType) {
 #' @details Get the scenario name to use in ensemble runs
 #' @param aScenName Scenario base name
 #' @param aExpectation Expectation type
-#' @param aYears Years for Lagged or Linear expectations (NULL for Perfect)
+#' @param aYears Years for Adaptive or Linear expectations (NULL for Perfect)
 #' @param aAgFor Logit exponent for AgroForestLand
 #' @param aAgForNonPast Logit exponent for AgroForestLand_NonPasture
 #' @param aCrop Logit exponent for Cropland
@@ -84,9 +84,9 @@ getScenName <- function(aScenName, aExpectation, aYears, aAgFor, aAgForNonPast,
   # Add expectation information
   if(aExpectation == "Linear") {
     scenNameAdj <- paste(aScenName, "_", aExpectation, aYears, sep="")
-  } else if (aExpectation == "Lagged") {
+  } else if (aExpectation == "Adaptive") {
     scenNameAdj <- paste(aScenName, "_", aExpectation, aYears, sep="")
-  } else if (aExpectation == "Mixed") {
+  } else if (aExpectation == "HybridLinearAdaptive") {
     scenNameAdj <- paste(aScenName, "_", aExpectation, aYears, sep="")
   } else {
     scenNameAdj <- paste(aScenName, "_", aExpectation, sep="")

R/main.R

@@ -6,8 +6,8 @@
 #' Parameter combinations are selected by generating a quasi-random
 #' sequence and mapping it to a specified range for each parameter.
 #' Then, each parameter set is run through the offline land model in
-#' each of the Perfect, Lagged, and Linear variants.  (I.e., if N
-#' parameter sets are selected, then 3N scenarios are run.)
+#' each of the Perfect, Adaptive, HybridPerfectAdaptive, HybridLinearAdaptive, and Linear variants.
+#'  (I.e., if N parameter sets are selected, then 5N scenarios are run.)
 #'
 #' This function is strictly for running the ensemble of models. Analysis
 #' must be completed after the fact.
@@ -58,7 +58,7 @@ run_ensemble  <- function(N = 500, aOutputDir = "./outputs", skip = 0,
   message("****************************************************")
 
   # Determine the number of parameters. If aDifferentiateParamByCrop = TRUE, then we have 3 parameters each for
-  # lagged share and linear years. If FALSE, then only one paramter for each. In both cases, there are 3 logit exponents
+  # lagged share and linear years. If FALSE, then only one parameter for each. In both cases, there are 3 logit exponents
   if( aDifferentiateParamByCrop ) {
     NPARAM <- 9
   } else {
@@ -187,8 +187,8 @@ run_ensemble  <- function(N = 500, aOutputDir = "./outputs", skip = 0,
 #' Parameter combinations are selected by generating a quasi-random
 #' sequence and mapping it to a specified range for each parameter.
 #' Then, each parameter set is run through the offline land model in
-#' each of the Perfect, Lagged, and Linear variants.  (I.e., if N
-#' parameter sets are selected, then 3N scenarios are run.)
+#' each of the Perfect, Adaptive, HybridLinearAdaptive, HybridPerfectAdaptive, and Linear variants.
+#' (I.e., if N parameter sets are selected, then 5N scenarios are run.)
 #'
 #' If the scenario type is "Hindcast", then after each model has been run, the
 #' Bayesian analysis will be run so that its results can be stored with the rest
@@ -364,7 +364,7 @@ run_ensemble_bayes  <- function(N = 500, aOutputDir = "./outputs", skip = 0,
 
 #' Generate the ensemble members for a single set of parameters
 #'
-#' This generates one each of the Perfect, Lagged, and Linear scenario types
+#' This generates one each of the Perfect, Adaptive, HybridLinearAdaptive, HybridPerfectAdaptive, and Linear scenario types
 #' using the input parameters.  The return value is a list of the three
 #' \code{ScenarioInfo} objects for the scenarios generated.
 #'
@@ -411,12 +411,12 @@ gen_ensemble_member <- function(agFor, agForNonPast, crop, share1, share2, share
                            aOutputDir = aOutputDir)
 
 
-  ## Lagged scenario - without including current prices (i.e., y[i] = a*y[i-1] + (1-a)*x[i-1])
+  ## Adaptive scenario - without including current prices (i.e., y[i] = a*y[i-1] + (1-a)*x[i-1])
   share <- paste(share1, share2, share3, sep="-")
-  scenName <- getScenName(aScenType, "Lagged", share, agFor, agForNonPast, crop)
+  scenName <- getScenName(aScenType, "Adaptive", share, agFor, agForNonPast, crop)
 
   lagscen <- ScenarioInfo(aScenarioType = aScenType,
-                          aExpectationType = "Lagged",
+                          aExpectationType = "Adaptive",
                           aLinearYears1 = NA,
                           aLinearYears2 = NA,
                           aLinearYears3 = NA,
@@ -433,12 +433,12 @@ gen_ensemble_member <- function(agFor, agForNonPast, crop, share1, share2, share
                           aSerialNum = serialnum+0.2,
                           aOutputDir = aOutputDir)
 
-  ## Lagged scenario - with including current prices (i.e., y[i] = a*y[i-1] + (1-a)*x[i])
+  ## HybridPerfectAdaptive scenario - with including current prices (i.e., y[i] = a*y[i-1] + (1-a)*x[i])
   share <- paste(share1, share2, share3, sep="-")
-  scenName <- getScenName(aScenType, "LaggedCurr", share, agFor, agForNonPast, crop)
+  scenName <- getScenName(aScenType, "HybridPerfectAdaptive", share, agFor, agForNonPast, crop)
 
   lagcurrscen <- ScenarioInfo(aScenarioType = aScenType,
-                          aExpectationType = "LaggedCurr",
+                          aExpectationType = "HybridPerfectAdaptive",
                           aLinearYears1 = NA,
                           aLinearYears2 = NA,
                           aLinearYears3 = NA,
@@ -479,9 +479,9 @@ gen_ensemble_member <- function(agFor, agForNonPast, crop, share1, share2, share
   ## mixed scenario, using linear for yield and adaptive for prices
   linyears <- paste(linyears1, linyears2, linyears3, sep="-")
   share <- paste(share1, share2, share3, sep="-")
-  scenName <- getScenName(aScenType, "Mixed", paste(linyears, share, sep="_"), agFor, agForNonPast, crop)
+  scenName <- getScenName(aScenType, "HybridLinearAdaptive", paste(linyears, share, sep="_"), agFor, agForNonPast, crop)
   mixedscen <- ScenarioInfo(aScenarioType = aScenType,
-                          aExpectationType = "Mixed",
+                          aExpectationType = "HybridLinearAdaptive",
                           aLinearYears1 = linyears1,
                           aLinearYears2 = linyears2,
                           aLinearYears3 = linyears3,

R/process_hindcast_data.R

@@ -71,7 +71,7 @@ get_historic_yields <- function(){
 #' @export
 get_hindcast_AgProdChange <- function(aScenType){
   # Silence package checks
-  year <- prev_year <- GCAM_commodity <- yield.x <- yield.y <- region <- AgProdChange <- NULL
+  year <- prev_year <- GCAM_commodity <- yield <- yield.x <- yield.y <- region <- AgProdChange <- NULL
 
     # Compute AgProdChange
   if(aScenType != "Hindcast5yr") {

data-raw/stats-testdata.R

@@ -79,7 +79,7 @@ gen_stats_testdata <- function()
     limits <- c(0, 6)
     scentype <- 'Hindcast'
     exptype1 <- 'Perfect'
-    exptype2 <- 'Lagged'
+    exptype2 <- 'Adaptive'
 
     rn <- randtoolbox::sobol(Nsample, NPARAM)
     p <- limits[1] + rn*(limits[2]-limits[1])