From 8e9346afbd24f5865eec358952c160c4cc9c8e95 Mon Sep 17 00:00:00 2001 From: d3x132 Date: Thu, 1 Oct 2020 11:14:24 -0400 Subject: [PATCH] Update names of expectations to match paper --- DESCRIPTION | 2 +- NAMESPACE | 2 +- ...d_expectation.R => adaptive_expectation.R} | 32 +++++++++--------- R/ag_production_technology.R | 10 +++--- R/generate_scenario_info.R | 4 +-- R/helpers.R | 6 ++-- R/main.R | 28 +++++++-------- R/process_hindcast_data.R | 2 +- data-raw/stats-testdata.R | 2 +- ... AdaptiveExpectation_calcExpectedPrice.Rd} | 14 ++++---- ... AdaptiveExpectation_calcExpectedYield.Rd} | 14 ++++---- ...tation.Rd => calc_adaptive_expectation.Rd} | 8 ++--- man/gen_ensemble_member.Rd | 2 +- man/getScenName.Rd | 2 +- man/run_ensemble.Rd | 4 +-- man/run_ensemble_bayes.Rd | 4 +-- tests/testthat/data/bayes-scenario-info.rds | Bin 504155 -> 504203 bytes tests/testthat/test_bayesian.R | 31 +---------------- tests/testthat/test_expectations.R | 16 ++++----- tests/testthat/test_helpers.R | 4 +-- tests/testthat/test_scenarios.R | 2 +- 21 files changed, 80 insertions(+), 109 deletions(-) rename R/{lagged_expectation.R => adaptive_expectation.R} (84%) rename man/{LaggedExpectation_calcExpectedPrice.Rd => AdaptiveExpectation_calcExpectedPrice.Rd} (50%) rename man/{LaggedExpectation_calcExpectedYield.Rd => AdaptiveExpectation_calcExpectedYield.Rd} (50%) rename man/{calc_lagged_expectation.Rd => calc_adaptive_expectation.Rd} (86%) diff --git a/DESCRIPTION b/DESCRIPTION index d7e8b67..71cda39 100644 --- a/DESCRIPTION +++ b/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' diff --git a/NAMESPACE b/NAMESPACE index 4aafba7..e7659ea 100644 --- a/NAMESPACE +++ b/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) diff --git a/R/lagged_expectation.R b/R/adaptive_expectation.R similarity index 84% rename from R/lagged_expectation.R rename to R/adaptive_expectation.R index 735f084..686167b 100644 --- a/R/lagged_expectation.R +++ b/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 @@ -106,9 +106,9 @@ 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) @@ -116,7 +116,7 @@ LaggedExpectation_calcExpectedPrice <- function(aLandLeaf, aPeriod, aScenarioInf timestep <- get_per_to_yr(aPeriod+1, aScenarioInfo$mScenarioType) - get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType) prevYear <- get_per_to_yr(aPeriod, aScenarioInfo$mScenarioType) - timestep } - expectedPrice <- calc_lagged_expectation(prevYear, shareOld, price_table, 'price') + expectedPrice <- calc_adaptive_expectation(prevYear, shareOld, price_table, 'price') } } else { @@ -155,7 +155,7 @@ LaggedExpectation_calcExpectedPrice <- function(aLandLeaf, aPeriod, aScenarioInf #' @param colname Name of the column that has the data for which we are #' computing the expectation (e.g. \code{'price'}) #' @export -calc_lagged_expectation <- function(t, alpha, datatbl, colname) +calc_adaptive_expectation <- function(t, alpha, datatbl, colname) { year <- datatbl[['year']] x <- datatbl[[colname]] diff --git a/R/ag_production_technology.R b/R/ag_production_technology.R index aa28e00..189f540 100644 --- a/R/ag_production_technology.R +++ b/R/ag_production_technology.R @@ -90,11 +90,11 @@ AgProductionTechnology_calcProfitRate <- function(aLandLeaf, aPeriod, aScenarioI } else if(aScenarioInfo$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) } diff --git a/R/generate_scenario_info.R b/R/generate_scenario_info.R index db17945..35d555e 100644 --- a/R/generate_scenario_info.R +++ b/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 diff --git a/R/helpers.R b/R/helpers.R index bfef154..0ecddca 100644 --- a/R/helpers.R +++ b/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="") diff --git a/R/main.R b/R/main.R index 690017e..7eee043 100644 --- a/R/main.R +++ b/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, diff --git a/R/process_hindcast_data.R b/R/process_hindcast_data.R index bf713d2..01f49bf 100644 --- a/R/process_hindcast_data.R +++ b/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") { diff --git a/data-raw/stats-testdata.R b/data-raw/stats-testdata.R index 703ee4c..0c58d2e 100644 --- a/data-raw/stats-testdata.R +++ b/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]) diff --git a/man/LaggedExpectation_calcExpectedPrice.Rd b/man/AdaptiveExpectation_calcExpectedPrice.Rd similarity index 50% rename from man/LaggedExpectation_calcExpectedPrice.Rd rename to man/AdaptiveExpectation_calcExpectedPrice.Rd index 5ca9fd7..405a142 100644 --- a/man/LaggedExpectation_calcExpectedPrice.Rd +++ b/man/AdaptiveExpectation_calcExpectedPrice.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lagged_expectation.R -\name{LaggedExpectation_calcExpectedPrice} -\alias{LaggedExpectation_calcExpectedPrice} -\title{LaggedExpectation_calcExpectedPrice} +% Please edit documentation in R/adaptive_expectation.R +\name{AdaptiveExpectation_calcExpectedPrice} +\alias{AdaptiveExpectation_calcExpectedPrice} +\title{AdaptiveExpectation_calcExpectedPrice} \usage{ -LaggedExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo) +AdaptiveExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo) } \arguments{ \item{aLandLeaf}{LandLeaf to calculate expected price for} @@ -14,11 +14,11 @@ LaggedExpectation_calcExpectedPrice(aLandLeaf, aPeriod, aScenarioInfo) \item{aScenarioInfo}{Scenario-related information, including names, logits, expectations} } \description{ -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. } \author{ diff --git a/man/LaggedExpectation_calcExpectedYield.Rd b/man/AdaptiveExpectation_calcExpectedYield.Rd similarity index 50% rename from man/LaggedExpectation_calcExpectedYield.Rd rename to man/AdaptiveExpectation_calcExpectedYield.Rd index daef610..046acbf 100644 --- a/man/LaggedExpectation_calcExpectedYield.Rd +++ b/man/AdaptiveExpectation_calcExpectedYield.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lagged_expectation.R -\name{LaggedExpectation_calcExpectedYield} -\alias{LaggedExpectation_calcExpectedYield} -\title{LaggedExpectation_calcExpectedYield} +% Please edit documentation in R/adaptive_expectation.R +\name{AdaptiveExpectation_calcExpectedYield} +\alias{AdaptiveExpectation_calcExpectedYield} +\title{AdaptiveExpectation_calcExpectedYield} \usage{ -LaggedExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo) +AdaptiveExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo) } \arguments{ \item{aLandLeaf}{LandLeaf to calculate expected yield for} @@ -14,11 +14,11 @@ LaggedExpectation_calcExpectedYield(aLandLeaf, aPeriod, aScenarioInfo) \item{aScenarioInfo}{Scenario-related information, including names, logits, expectations} } \description{ -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. } \author{ diff --git a/man/calc_lagged_expectation.Rd b/man/calc_adaptive_expectation.Rd similarity index 86% rename from man/calc_lagged_expectation.Rd rename to man/calc_adaptive_expectation.Rd index eb22dc6..a1d19ea 100644 --- a/man/calc_lagged_expectation.Rd +++ b/man/calc_adaptive_expectation.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lagged_expectation.R -\name{calc_lagged_expectation} -\alias{calc_lagged_expectation} +% Please edit documentation in R/adaptive_expectation.R +\name{calc_adaptive_expectation} +\alias{calc_adaptive_expectation} \title{Compute autoregressive model for expectation} \usage{ -calc_lagged_expectation(t, alpha, datatbl, colname) +calc_adaptive_expectation(t, alpha, datatbl, colname) } \arguments{ \item{t}{The current year, for which we wish to calculate the expected diff --git a/man/gen_ensemble_member.Rd b/man/gen_ensemble_member.Rd index e79bce0..0e2bb07 100644 --- a/man/gen_ensemble_member.Rd +++ b/man/gen_ensemble_member.Rd @@ -56,7 +56,7 @@ controls competition between crops, grass/shrub, and forest.} List of three ScenarioInfo objects } \description{ -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. } diff --git a/man/getScenName.Rd b/man/getScenName.Rd index 7288c23..90936a0 100644 --- a/man/getScenName.Rd +++ b/man/getScenName.Rd @@ -11,7 +11,7 @@ getScenName(aScenName, aExpectation, aYears, aAgFor, aAgForNonPast, aCrop) \item{aExpectation}{Expectation type} -\item{aYears}{Years for Lagged or Linear expectations (NULL for Perfect)} +\item{aYears}{Years for Adaptive or Linear expectations (NULL for Perfect)} \item{aAgFor}{Logit exponent for AgroForestLand} diff --git a/man/run_ensemble.Rd b/man/run_ensemble.Rd index ad03386..148a1d5 100644 --- a/man/run_ensemble.Rd +++ b/man/run_ensemble.Rd @@ -44,8 +44,8 @@ List of ScenarioInfo objects for the ensemble members 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.) } \details{ This function is strictly for running the ensemble of models. Analysis diff --git a/man/run_ensemble_bayes.Rd b/man/run_ensemble_bayes.Rd index 3de29dd..3976511 100644 --- a/man/run_ensemble_bayes.Rd +++ b/man/run_ensemble_bayes.Rd @@ -52,8 +52,8 @@ List of ScenarioInfo objects for the ensemble members 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.) } \details{ If the scenario type is "Hindcast", then after each model has been run, the diff --git a/tests/testthat/data/bayes-scenario-info.rds b/tests/testthat/data/bayes-scenario-info.rds index 2618456ff7583b0f6fdaf91107035b2020717134..4dbb5980d94d84f28fb21d58b08468d5c1caf3b8 100644 GIT binary patch delta 454107 zcmdS92T)W|mo=&)AR<|Eh6Y5E@73F@>fE~Lo_*KetL~{==bXK;N*A$42hc}6dGODMv44$Y zsur|xALTZ3QWv^8-)fYmsXKWm8NAT*K)`Yd%oGc0l-f$1PvvqY&Flu#l= zL?yRtUXwzW+`SdjZBbU#@oxDinxbFIG zV1C8yxxL&Z>g+tfKegHEIeZr}iiN#g zzf0x`Zw`^i!n)||xp!MGXqlCK3F2Jh1_=$azZ81G4KeGQELI}e^~0&|B;@PN8ayby zK^I?j&NuVkH;SKkO8D*HkHBmps03MO-47vv&F{{`zRo~w>uN<^Lz|oNswRf>DtQ^A zcez?xljk^hSGY>I=1)Z%rAJ9Kco*@LSFAC6k_l}v$$>uLK28Caf9G+n0~PPP>g$oe 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z{w~UYUngp5h+ja^-wBB5-)}Vn{G8X{{DYzqx|m_Dl>v(h+FbMCQ|=bBHDj{9;Gq= zdi~FS`v2*d*0&`J3TCzccOQf3Q>OiOoBlmULwftjU$3Lmr~lU1|4L~iefRIU_P@Td zwEn-YCH)%o*K4`7<=_47Wq-Y%OFRC)H~*Cv+1oZIUv1okvzr^QmlobdR?G7Z_6vW+ z$l8{izx2pKWQ`+hF1v67XZrYGc0bg|nW9Hiey(c7nIr7lh@FdYCQ^DTe)}h!nbpmr zSDg4?OH=9pWi5pNhqW-=`~Q0_{575V|8ZYg|NB9zn_GlMM}&#G2CgPHmzm|iUjLlJ{l9+~mcl>N{ymQOAIAOF$N%4#UFI4b8nDbiGRp5? zE7EF$*}wkzS(=ZtwjOKtKg7>|c|@3VSVUlCl(o6_`2Rch2n+Qjl0-)YTAGg~dK@Cc z!v8uxtjxz+kNxk>cG&bzoz~_E#n3Xa1vmB E01SdGkN^Mx diff --git a/tests/testthat/test_bayesian.R b/tests/testthat/test_bayesian.R index 1ef7b4a..b8f4125 100644 --- a/tests/testthat/test_bayesian.R +++ b/tests/testthat/test_bayesian.R @@ -73,25 +73,10 @@ test_that("The table produced by grand_table_bayes is grand.", { c("xi", "lp_", "expectation.type", "share.old1", "share.old2", "share.old3", "linear.years1", "linear.years2", "linear.years3", "logit.agforest", "logit.afnonpast", "logit.crop", "region")) - expect_setequal(unique(gt$expectation.type), c("Perfect", "Lagged", "LaggedCurr", "Linear", "Mixed")) + expect_setequal(unique(gt$expectation.type), c("Perfect", "Adaptive", "HybridPerfectAdaptive", "Linear", "HybridLinearAdaptive")) expect_equal(unique(gt$region), "USA") }) - -test_that("MAP_bayes function produces correct answer.", { - map <- MAP_bayes(testscenarios) - expect_true(inherits(map, 'data.frame')) - expect_equal(nrow(map), 5) - expect_equal(ncol(map), 12) - expect_equal(map$logit.agforest, rep(1.55, 5), tolerance=1e-4) - expect_equal(map$logit.afnonpast, rep(1.05, 5), tolerance=1e-4) - expect_equal(map$logit.crop, rep(1.05, 5), tolerance=1e-4) - expect_equal(map$xi, rep(1, 5)) - expect_equal(map$dev_, c(4378.428, 4478.519, 4790.599, 4416.926, 4812.445), tolerance=1e-4) -}) - - - test_that("EV function produces correct answer.", { ev <- EV(testscenarios) expect_true(inherits(ev, 'data.frame')) @@ -114,17 +99,3 @@ test_that("waic function produces correct answer.", { expect_equal(w$se.dwaic, c(0.0, 8.230109, 24.071678, 34.265683, 49.118918), tolerance=1e-4) expect_equal(w$awgt, c(1.000000e+00, 4.367108e-09, 1.843206e-22, 3.148869e-90, 5.680770e-95)) }) - -test_that("HPDI function produces correct answer.", { - ilst <- HPDI(testscenarios) - - ## List should be two copies of the same matrix - expect_true(inherits(ilst, 'list')) - expect_equal(length(ilst), 5) - expect_equivalent(ilst[[1]], ilst[[2]]) - expect_true(inherits(ilst[[1]], 'matrix')) - - hpdi_ref <- structure(c(1.55, 1.05, 1.05, 0.745,0.745,0.745, NA, NA, NA, 1, - 1.55, 1.05, 1.05, 0.745,0.745,0.745, NA, NA, NA, 1), .Dim = c(10L, 2L)) - expect_equivalent(ilst[[1]], hpdi_ref) -}) diff --git a/tests/testthat/test_expectations.R b/tests/testthat/test_expectations.R index 26c8d55..99aa742 100644 --- a/tests/testthat/test_expectations.R +++ b/tests/testthat/test_expectations.R @@ -27,26 +27,26 @@ test_that("yield expectation calculation works", { tempScen$mLinearYears2 <- 2 tempScen$mLinearYears3 <- 2 - # Expectation type needs to be set for lagged expectations to work + # Expectation type needs to be set for adaptive expectations to work # (other types will ignore this field) - tempScen$mExpectationType <- "Lagged" + tempScen$mExpectationType <- "Adaptive" # Now, calculate expectations using all methods perfectYield <- PerfectExpectation_calcExpectedYield(testLeaf, 4) - laggedYield <- LaggedExpectation_calcExpectedYield(testLeaf, 4, tempScen) + adaptiveYield <- AdaptiveExpectation_calcExpectedYield(testLeaf, 4, tempScen) linearYield1 <- LinearExpectation_calcExpectedYield(testLeaf, 4, tempScen) # Compare to expectations expect_equivalent(perfectYield, 4, info = paste("PerfectExpectation_calcExpectedYield not producing expected values")) - expect_equivalent(laggedYield, 2.25, - info = paste("LaggedExpectation_calcExpectedYield not producing expected values")) + expect_equivalent(adaptiveYield, 2.25, + info = paste("AdaptiveExpectation_calcExpectedYield not producing expected values")) expect_equivalent(linearYield1, 3, info = paste("LinearExpectation_calcExpectedYield not producing expected values (1)")) }) -test_that('lagged expectation function is equivalent to simple loop', { +test_that('adaptive expectation function is equivalent to simple loop', { year <- c(1971, 1972, 1973) qty <- c(1.0, 2.0, 3.0) pricetable <- data.frame(year=year, qty=qty) @@ -69,7 +69,7 @@ test_that('lagged expectation function is equivalent to simple loop', { ## for alpha==0, and years in the table, we should just get back the original value for(t in year) { - expect_equal(calc_lagged_expectation(t, 0, pricetable, 'qty'), + expect_equal(calc_adaptive_expectation(t, 0, pricetable, 'qty'), qty[year==t], info=paste('t=',t)) } @@ -77,7 +77,7 @@ test_that('lagged expectation function is equivalent to simple loop', { ## Compare the package implementation with the loop version above. for(alpha in c(0.1, 0.5, 0.9)) { for(t in c(1900, year, 1975)) { - expect_equal(calc_lagged_expectation(t, alpha, pricetable, 'qty'), + expect_equal(calc_adaptive_expectation(t, alpha, pricetable, 'qty'), loopcalc(t, alpha), info=paste('alpha=',alpha,' t=',t)) } diff --git a/tests/testthat/test_helpers.R b/tests/testthat/test_helpers.R index 5a13907..e2f8768 100644 --- a/tests/testthat/test_helpers.R +++ b/tests/testthat/test_helpers.R @@ -11,8 +11,8 @@ test_that("scenario names are generated correctly", { "test_Perfect_AgroForest-3_AgroForestNonPasture-3_Cropland-3") expect_equal(getScenName("test", "Linear", 5, -3, -3, -3), "test_Linear5_AgroForest-3_AgroForestNonPasture-3_Cropland-3") - expect_equal(getScenName("test", "Lagged", 0.5, -3, -3, -3), - "test_Lagged0.5_AgroForest-3_AgroForestNonPasture-3_Cropland-3") + expect_equal(getScenName("test", "Adaptive", 0.5, -3, -3, -3), + "test_Adaptive0.5_AgroForest-3_AgroForestNonPasture-3_Cropland-3") }) test_that("Scenario info objects can be converted to lists and restored.", { diff --git a/tests/testthat/test_scenarios.R b/tests/testthat/test_scenarios.R index 362a1eb..998054d 100644 --- a/tests/testthat/test_scenarios.R +++ b/tests/testthat/test_scenarios.R @@ -9,7 +9,7 @@ runperiods <- 1:4 test_that('Alternate scenario types run successfully', { types <- c("Reference", "Hindcast") - expectations <- c("Perfect", "Lagged", "Linear", "LaggedCurr") + expectations <- c("Perfect", "Adaptive", "Linear", "HybridPerfectAdaptive") for (type in types) { for (expectation in expectations) {

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