In USGS-R/EGRETci: Exploration and Graphics for RivEr Trends Confidence Intervals

knitr::opts_chunk$set(
  echo = TRUE,
  eval = TRUE, # Change this later!
  message = FALSE,
  warning = FALSE,
  fig.width = 7, 
  fig.height = 4
)

See https://doi-usgs.github.io/EGRET/articles/parallel.html for an introduction to running in parallel in EGRET. These directions supplement that article for EGRETci functions.

library(EGRET)
library(EGRETci)
library(parallel)
library(doParallel)

eList <- Choptank_eList
nCores <- detectCores(logical = FALSE) - 2 # leave a core or two out
nCores <- max(c(nCores, 1))
nCores

doParellel

A generalized workflow uses the doParallel package:

cl <- parallel::makeCluster(nCores)
doParallel::registerDoParallel(cl)
eList <- modelEstimation(eList, 
                         verbose = FALSE,
                         run.parallel = TRUE)
parallel::stopCluster(cl)

Calculating Confidence Intervals

In series:

nBoot <- 20 # Let's make sure things run with a small nBoot
# but bump up later!

blockLength <- 200

repAnnualResults <- vector(mode = "list", length = nBoot)

for(n in 1:nBoot){
   annualResults <- bootAnnual(eList, 
                               blockLength, 
                               startSeed = n, 
                               verbose = FALSE)
   repAnnualResults[[n]] <- annualResults
}

CIAnnualResults <- ciBands(eList, 
                           repAnnualResults)

plotConcHistBoot(eList, CIAnnualResults)

In parallel:

cl <- parallel::makeCluster(nCores)
doParallel::registerDoParallel(cl)
repAnnual <- foreach(n = 1:nBoot,
                     .packages=c('EGRETci', 'EGRET')) %dopar% {
   annualResults <- bootAnnual(eList, 
                               blockLength,
                               startSeed = n, 
                               verbose = FALSE)  
}
parallel::stopCluster(cl)   

CIAnnualResults_p <- ciBands(eList, repAnnual)
plotConcHistBoot(eList, CIAnnualResults_p)

runPairs

In series

year1 <- 1985
year2 <- 2010

pairOut_2 <- runPairs(eList, year1, year2, windowSide = 11, 
                      verbose = FALSE)

boot_pair_out <- runPairsBoot(eList, pairOut_2, 
                              nBoot = nBoot)
plotHistogramTrend(eList = eList, eBoot = boot_pair_out,
                   xMin = -5, xMax = 60, xStep = 5,
                   caseSetUp = NA)

In parallel:

cl <- parallel::makeCluster(nCores)
doParallel::registerDoParallel(cl)

boot_pair_out <- runPairsBoot(eList, pairOut_2,
                              nBoot = nBoot, 
                              run.parallel = TRUE)
parallel::stopCluster(cl)

plotHistogramTrend(eList = eList, 
                   eBoot = boot_pair_out,
                   xMin = -5, xMax = 60, xStep = 5,
                   caseSetUp = NA)

runPairs

In series

groupResults <- runGroups(eList,
                          group1firstYear = 1995,
                          group1lastYear = 2004,
                          group2firstYear = 2005,
                          group2lastYear = 2014,
                          windowSide = 7, wall = TRUE,
                          sample1EndDate = "2004-10-30",
                          paStart = 4, paLong = 2,
                          verbose = FALSE)

boot_group_out <- runGroupsBoot(eList, 
                               groupResults,
                               nBoot = nBoot)

In parallel:

cl <- parallel::makeCluster(nCores)
doParallel::registerDoParallel(cl)

boot_group_out <- runGroupsBoot(eList = eList,
                                groupResults = groupResults,
                                nBoot = nBoot,
                                run.parallel = TRUE)
parallel::stopCluster(cl)