Type:	Package
Title:	Exploration and Graphics for RivEr Trends Confidence Intervals
Version:	2.0.5
Description:	Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.
License:	CC0
Depends:	R (≥ 3.5.0)
Imports:	EGRET(≥ 3.0.5), binom, stats, graphics, utils, foreach
Suggests:	knitr, testthat, doParallel, iterators, rmarkdown, pkgdown, covr
LazyLoad:	yes
LazyData:	yes
VignetteBuilder:	knitr
BuildVignettes:	true
BugReports:	https://github.com/DOI-USGS/EGRETci/issues
Copyright:	This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at hhttps://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits
RoxygenNote:	7.3.2
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2025-04-17 19:03:07 UTC; ldecicco
Author:	Laura DeCicco [aut, cre], Robert Hirsch [aut], Stacey Archfield [ctb], Jennifer Murphy [ctb]
Maintainer:	Laura DeCicco <ldecicco@usgs.gov>
Repository:	CRAN
Date/Publication:	2025-04-17 20:00:01 UTC

EGRETci package for bootstrap hypothesis tests and confidence interval analysis for WRTDS (Weighted Regressions on Time, Discharge, and Season) statistical models. This package is designed to be used in conjunction with the EGRET package, which estimates and describes WRTDS models.

Description

Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis.

Author(s)

Robert M. Hirsch rhirsch@usgs.gov, Laura De Cicco ldecicco@usgs.gov

References

Hirsch, R.M., and De Cicco, L.A., 2015, User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data: U.S. Geological Survey Techniques and Methods book 4, chap. A10, 94 p., doi:10.3133/tm4A10

Hirsch, R.M., Archfield, S.A., and De Cicco, L.A., 2015, A bootstrap method for estimating uncertainty of water quality trends. Environmental Modelling & Software, 73, 148-166.

See Also

Useful links:

• Report bugs at https://github.com/DOI-USGS/EGRETci/issues

Example eBoot

Description

Example data representing data from the Choptank River at Greensboro, MD, USGS data Data is a named list of the Daily, Sample, INFO dataframes, and xConc, and xFlux vectors.

blockSample

Description

Get a bootstrap replicate of the Sample data frame based on the user-specified blockLength. The bootstrap replicate is made up randomly selected blocks of data from Sample data frame. Each block includes all the samples in a standard period of time (the blockLength measured in days). The blocks are created based on the random selection (with replacement) of starting dates from the full Sample data frame. The bootstrap replicate has the same number of observations as the original Sample, but some observations are included once, some are included multiple times, and some are not included at all.

Usage

blockSample(localSample, blockLength, startSeed = NA)

Arguments

Value

newSample data frame in same format as Sample data frame. It has the same number of rows as the Sample data frame.

Examples

library(EGRET)
eList <- Choptank_eList
Sample <- eList$Sample
bsReturn <- blockSample(Sample, 200)

Single confidence interval bootstrap run

Description

One bootstrap run used in calculating confidence interval bands.

Usage

bootAnnual(eList, blockLength = 200, startSeed = 494817, verbose = FALSE,
  jitterOn = FALSE, V = 0.2)

Arguments

Details

In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.

Examples

library(EGRET)
eList <- Choptank_eList
## Not run: 
annualResults <- bootAnnual(eList)

## End(Not run)

Print message of bootstrapping results

Description

Uses the results of either group or pair results and prints of the standard message.

Usage

boot_message(eList, type_results, bootOut, type = "pair")

Arguments

Examples

eList <- EGRET::Choptank_eList
year1 <- 1985
year2 <- 2009

## Not run: 
pairOut_2 <- EGRET::runPairs(eList, 
                             year1, year2, 
                             windowSide = 7)

# For good analysis, bump up nBoot to about 100:
boot_pair_out <- runPairsBoot(eList, pairOut_2, nBoot = 5)
boot_message(eList,
             pairOut_2,
             boot_pair_out)

## End(Not run)

Calculations from boot return

Description

Calculations from boot return

Usage

calc_boot_out(boot_list_return, type_results, nBoot, startSeed, blockLength)

Arguments

Confidence Interval Band Calculations

Description

Computes confidence intervals for Flow-Normalized Concentration and Flow-Normalized Flux for a WRTDS model.

Usage

ciBands(eList, repAnnualResults, probs = c(0.05, 0.95))

Arguments

Examples

library(EGRET)
eList <- Choptank_eList
nBoot <- 100
blockLength <- 200
## Not run: 

repAnnualResults <- vector(mode = "list", length = nBoot)
for(n in 1:nBoot){
   annualResults <- bootAnnual(eList, blockLength, startSeed = n) 
   repAnnualResults[[n]] <- annualResults
}

CIAnnualResults <- ciBands(eList, repAnnualResults)
plotConcHistBoot(eList, CIAnnualResults)

## End(Not run)

ciCalculations

Description

Function to calculate confidence bands for flow-normalized concentration or flow-normalized flux. It returns a data frame which is used as input to the functions plotConcHistBoot and plotFluxHistBoot which produce the graphical output.

Usage

ciCalculations(eList, startSeed = 494817, verbose = TRUE,
  jitterOn = FALSE, V = 0.2, nBoot = 100, blockLength = 200,
  widthCI = 90)

Arguments

Details

In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.

Argument values suggested. To test the code, nBoot = 10 is sufficient, but for meaningful results nBoot = 100 or even nBoot = 500 are more appropriate. blockLength = 200. widthCI = 90 (90

Value

A data frame with the following columns:

Examples

library(EGRET)
eList <- Choptank_eList
## Not run: 
CIAnnualResults <- ciCalculations(eList,
                                  nBoot = 10)
plotConcHistBoot(eList, CIAnnualResults)

# run in batch mode, using non-stationary flow normalization
# In this example nBoot is set very small, useful for an initial trial run.
# A meaningful application would use nBoot values such as 100 or even 500. 
seriesOut_2 <- runSeries(eList, windowSide = 11)
CIAnnualResults <- ciCalculations(seriesOut_2, 
                     nBoot = 10,
                     blockLength = 200,
                     widthCI = 90)
                     
 plotConcHistBoot(seriesOut_2, CIAnnualResults)


## End(Not run)

WRTDSKalman Bootstrapping

Description

Function to get multiple bootstrap replicates at a daily time step using the WRTDS_K method. It is done by doing bootstrap resampling of the original Sample data frame. The number of these replicate samples that are created is called nBoot and in each case the WRTDS model is estimated. Then, for each of these models, there are nKalman time series of daily values computed, using all of the sample values in the original Sample data frame. The total number of replicates of the complete process is nBoot * nKalman. For example we might generate 500 replicates by setting nBoot = 20 and nKalman = 25.

Usage

genDailyBoot(eList, nBoot = 10, nKalman = 10, rho = 0.9, setSeed = NA,
  jitterOn = FALSE, V = 0.2)

Arguments

Details

In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.

Value

dailyBootOut a matrix of daily flux values (in kg/day). The number of columns of the matrix is the number of replicates produced which is nBoot * nKalman The number of rows is the number of days in the record. The set of days simulated is the same set of days that are in the eList$Daily data frame.

Examples

eList <- EGRET::Choptank_eList
# Very long running function:
## Not run: 
dailyBootOut <- genDailyBoot(eList,
                             nBoot = 20,
                             nKalman = 25)

## End(Not run)

Make Annual Prediction Intervals

Description

This function takes the output from genDailyBoot and calculates the quantiles for an annual (based on paStart/paLong) aggregation. This means that the function can be used for seasons.

Usage

makeAnnualPI(dailyBootOut, eList, paLong = 12, paStart = 10,
  fluxUnit = 3)

Arguments

Value

a list of 2 data frames, one for average concentration, in mg/L and one for flux (unit depends on fluxUnit argument) In each data frame the first column is DecYear. The remaining columns are quantiles of the flux or concentration (depending on the data frame).

Examples

eList <- EGRET::Choptank_eList
# This example is only based on 4 iterations
# Actual prediction intervals should be calculated on
# a much larger number of iterations (several hundred).  
dailyBoot <- Choptank_dailyBootOut
annualPcts <- makeAnnualPI(dailyBoot, eList)
head(annualPcts[["flux"]])
head(annualPcts[["conc"]])

Make Daily Prediction Intervals

Description

This function takes the output from genDailyBoot and calculates the quantiles for a daily aggregation.

Usage

makeDailyPI(dailyBootOut, eList, fluxUnit = 3)

Arguments

Value

a list of 2 data frames, one for average concentration, in mg/L and one for flux (unit depends on fluxUnit argument) In each data frame the first column is Date. The remaining columns are quantiles of the flux or concentration (depending on the data frame).

Examples

eList <- EGRET::Choptank_eList
# This example is only based on 4 iterations
# Actual prediction intervals should be calculated on
# a much larger number of iterations (several hundred).
dailyBoot <- Choptank_dailyBootOut

dailyPcts <- makeDailyPI(dailyBoot, eList)
head(dailyPcts[["flux"]])
head(dailyPcts[["conc"]])

Make Monthly Prediction Intervals

Description

Month statistics using WRTDSKalman bootstrapping approach. The input to this function is the dailyBootOut matrix which contains nReplicate sets of daily flux values for the period of interest. The results are in the form of quantiles of concentration and of flux for each of these months.

Usage

makeMonthPI(dailyBootOut, eList, fluxUnit = 3)

Arguments

Value

a list of 2 data frames, one for average concentration, in mg/L and one for flux (unit depends on fluxUnit argument) In each data frame the first column is monthSeq that corresponds to the months in the "MonthSeq" column in the eList$Daily data frame. The remaining columns are quantiles of the flux or concentration (depending on the data frame).

Examples

eList <- EGRET::Choptank_eList
# This example is only based on 4 iterations
# Actual prediction intervals should be calculated on
# a much larger number of iterations (several hundred). 
dailyBoot <- Choptank_dailyBootOut
monthPcts <- makeMonthPI(dailyBoot, eList)
head(monthPcts[["flux"]])
head(monthPcts[["conc"]])

monthSeqToDec

Description

Convert a sequence of month integers into their decimal years.

Usage

monthSeqToDec(monthSeq)

Arguments

Examples

months <- 1558:1600
monthSeqToDec(months)

pVal

Description

Computes the two-sided p value for the null hypothesis, where the null hypothesis is that the slope is zero. It is based on the binomial distribution. Note that the result does not depend on the magnitude of the individual slope values only depends on the number of positive slopes and number of negative slopes.

Usage

pVal(s)

Arguments

Value

pVal numeric value

Examples

s <- c(-1.0, 0, 0.5, 0.55, 3.0)
pValue <- pVal(s)

Create a paVector

Description

Internal doc for paVector

Usage

paVector(year, paStart, paLong, vectorYear)

Arguments

Graph of annual concentration, flow normalized concentration, and confidence bands for flow normalized concentrations

Description

Uses the output of modelEstimation in the EGRET package (results in the named list eList), and the data frame CIAnnualResults (produced by the function ciCalculations in the EGRETci package using scripts described in the EGRETci vignette) to produce a graph of annual concentration, flow normalized concentration, and confidence bands for flow-normalized concentrations. In addition to the arguments listed below, it will accept any additional arguments that are listed for the EGRET function plotConcHist.

Usage

plotConcHistBoot(eList, CIAnnualResults, yearStart = NA, yearEnd = NA,
  plotFlowNorm = TRUE, col.pred = "green", concMax = NA,
  plotAnnual = TRUE, plotGenConc = FALSE, cex = 0.8, cex.axis = 1.1,
  lwd = 2, col = "black", col.gen = "red", customPar = FALSE,
  printTitle = TRUE, cex.main = 1.1, ...)

Arguments

Examples

library(EGRET)
eList <- Choptank_eList
CIAnnualResults <- Choptank_CIAnnualResults
plotConcHistBoot(eList, CIAnnualResults)
plotConcHistBoot(eList, CIAnnualResults, yearStart=1990, yearEnd=2002)
# Very long-running function:
## Not run: 
CIAnnualResults <- ciCalculations(eList, nBoot = 100, blockLength = 200)
plotConcHistBoot(eList, CIAnnualResults)

## End(Not run)

Graph of annual flux, flow normalized flux, and confidence bands for flow normalized flux

Description

Uses the output of modelEstimation in the EGRET package (results in the named list eList), and the data frame CIAnnualResults (produced by EGRETci package using scripts described in the vignette) to produce a graph of annual flux, flow normalized flux, and confidence bands for flow-normalized flux. In addition to the arguments listed below, it will accept any additional arguments that are listed for the EGRET function plotFluxHist.

Usage

plotFluxHistBoot(eList, CIAnnualResults, yearStart = NA, yearEnd = NA,
  fluxUnit = 9, fluxMax = NA, plotFlowNorm = TRUE, col.pred = "green",
  plotAnnual = TRUE, plotGenFlux = FALSE, cex = 0.8, cex.axis = 1.1,
  lwd = 2, col = "black", col.gen = "red", cex.main = 1.1,
  printTitle = TRUE, customPar = FALSE, ...)

Arguments

Examples

library(EGRET)
eList <- Choptank_eList 
CIAnnualResults <- Choptank_CIAnnualResults
plotFluxHistBoot(eList, CIAnnualResults, fluxUnit=5)

## Not run: 
CIAnnualResults <- ciCalculations(eList, nBoot = 100, blockLength = 200)
plotFluxHistBoot(eList, CIAnnualResults, fluxUnit=5)

## End(Not run)

plotHistogramTrend

Description

Produces a histogram of trend results from bootstrap process. The histogram shows the trend results expressed as percentage change between the first year (or first period) and the second year (or second period). It shows the zero line (no trend) and also shows the WRTDS estimate of the trend in percent. It is based on the output of either wBT or runPairsBoot.

Usage

plotHistogramTrend(eList, eBoot, caseSetUp = NA, flux = TRUE, xMin = NA,
  xMax = NA, xStep = NA, printTitle = TRUE, cex.main = 1.1,
  cex.axis = 1.1, cex.lab = 1.1, col.fill = "grey", ...)

Arguments

Details

For any given set of results (from eBoot) it is best to run it first with the arguments xMin = NA, xMax = NA, and xStep = NA. Then, observing the range the histogram covers it can be run again with values of these three arguments selected by the user to provide for a more readable version of the histogram.

Examples

library(EGRET)
eList <- Choptank_eList
eBoot <- Choptank_eBoot
caseSetUp <- Choptank_caseSetUp
plotHistogramTrend(eList, eBoot, caseSetUp, flux = FALSE)

## Not run: 
# Using wBT:	
caseSetUp <- trendSetUp(eList)
eBoot <- wBT(eList,caseSetUp)
plotHistogramTrend(eList, eBoot, caseSetUp,  
                   flux = FALSE, xMin = -20, xMax = 60, xStep = 5)
plotHistogramTrend(eList, eBoot, caseSetUp, 
                   flux = TRUE, xMin = -20, xMax = 60, xStep = 5)
   
# Using runPairs followed by runPairsBoot:
year1 <- 1985
year2 <- 2009          
pairOut_2 <- runPairs(eList, year1, year2, windowSide = 7)
boot_pair_out <- runPairsBoot(eList, pairOut_2, nBoot = 10)

plotHistogramTrend(eList, boot_pair_out, caseSetUp = NA, 
                   flux = TRUE, xMin = -20, xMax = 60, xStep = 5)          

## End(Not run)

The bootstrap uncertainty analysis for runGroups results

Description

This function that does the uncertainty analysis for determining the change between two groups of years. The process is virtually identical to what is used for runPairsBoot which looks at a change between a pair of years.

Usage

runGroupsBoot(eList, groupResults, nBoot = 100, startSeed = 494817,
  blockLength = 200, jitterOn = FALSE, V = 0.2, run.parallel = FALSE)

Arguments

Details

In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.

Value

eBoot, a named list with bootOut, wordsOut, xConc, xFlux, pConc, pFlux values.

• bootOut is a data frame with the results of the bootstrap test.

• wordsOut is a character vector describing the results.

• xConc and xFlux are vectors of length iBoot, of the change in flow normalized concentration and flow normalized flux computed from each of the bootstrap replicates.

• pConc and pFlux are vectors of length iBoot, of the change in flow normalized concentration or flow normalized flux computed from each of the bootstrap replicates expressed as % change.

See Also

runPairsBoot, runGroups

Examples

library(EGRET)
eList <- Choptank_eList

## Not run: 
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)

# For good analysis, bump up nBoot to about 100:
boot_group_out <- runGroupsBoot(eList, groupResults, nBoot = 10)

boot_message(eList, groupResults, boot_group_out)

plotHistogramTrend(eList, boot_group_out)


## End(Not run)

The bootstrap uncertainty analysis for runPairs results

Description

The function that does the uncertainty analysis for determining the change between any pair of years. It is very similar to the wBT function that runs the WRTDS bootstrap test. It differs from wBT in that it runs a specific number of bootstrap replicates, unlike the wBT approach that will stop running replicates based on the status of the test statistics along the way. Also, this code can be used with generalized flow normalization, which handles non-stationary discharge, whereas wBT does not.

Usage

runPairsBoot(eList, pairResults, nBoot = 100, startSeed = 494817,
  blockLength = 200, jitterOn = FALSE, V = 0.2, run.parallel = FALSE)

Arguments

Details

In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.

Value

eBoot, a named list with bootOut, wordsOut, xConc, xFlux, pConc, pFlux values.

• bootOut is a data frame with the results of the bootstrap test.

• wordsOut is a character vector describing the results.

• xConc and xFlux are vectors of length iBoot, of the change in flow normalized concentration and flow normalized flux computed from each of the bootstrap replicates.

• pConc and pFlux are vectors of length iBoot, of the change in flow normalized concentration or flow normalized flux computed from each of the bootstrap replicates expressed as % change.

See Also

runGroupsBoot, runPairs

Examples

eList <- EGRET::Choptank_eList
year1 <- 1985
year2 <- 2009

## Not run: 
pairOut_2 <- EGRET::runPairs(eList, 
                             year1, year2, 
                             windowSide = 7)

# For good analysis, bump up nBoot to about 100:
boot_pair_out <- runPairsBoot(eList, pairOut_2, nBoot = 5)

plotHistogramTrend(eList, boot_pair_out)

boot_message(eList, pairOut_2, boot_pair_out)

## End(Not run)

Run bootstrapping routine

Description

Depending on input arguments, this function will run a complete bootstrapping routine either in series or parallel.

Usage

run_bootstraps(eList, type_results, jitterOn, V, blockLength, startSeed, nBoot,
  type, run.parallel)

Arguments

Details

When in parallel, if there are unsuccessful runs, they will be made up in series after the parallel runs are done.#'

Save EGRETci workspace after running wBT (the WRTDS bootstrap test)

Description

Saves critical information in a EGRETci workflow when analyzing trends between a starting and ending year.

Usage

saveEGRETci(eList, eBoot, caseSetUp, fileName = "")

Arguments

Value

A .RData file containing three objects: eList, eBoot, and caseSetUp

See Also

wBT, trendSetUp, modelEstimation

Examples

eList <- EGRET::Choptank_eList
## Not run: 
caseSetUp <- trendSetUp(eList)
eBoot <- wBT(eList,caseSetUp)
saveEGRETci(eList, eBoot, caseSetUp)

## End(Not run)

Allows user to set window parameters for the WRTDS model prior to running the bootstrap procedure

Description

Adds window parameters to INFO file in eList.

Usage

setForBoot(eList, caseSetUp, windowY = 7, windowQ = 2, windowS = 0.5,
  edgeAdjust = TRUE)

Arguments

Value

eList list with Daily,Sample, INFO data frames and surface matrix.

Examples

eList <- EGRET::Choptank_eList

caseSetUp <- trendSetUp(eList,
  year1=1985, 
  year2=2005,
  nBoot = 50, 
  bootBreak = 39,
  blockLength = 200)

bootSetUp <- setForBoot(eList,caseSetUp)

Single Bootstrap Run

Description

Single run of a bootstrap.

Usage

single_boot_run(iBoot, startSeed, eList, type_results, jitterOn, V,
  blockLength, type)

Arguments

Value

list of xConc, xFlux, pConc, pFlux

Interactive setup for running wBT, the WRTDS Bootstrap Test

Description

Walks user through the set-up for the WRTDS Bootstrap Test. Establishes start and end year for the test period. Sets the minimum number of bootstrap replicates to be run, the maximum number of bootstrap replicates to be run, and the block length (in days) for the block bootstrapping. The test is designed to evaluate the uncertainty about the trend between any pair of years.

Usage

trendSetUp(eList, ...)

Arguments

Value

caseSetUp data frame with columns year1, yearData1, year2, yearData2, numSamples, nBoot, bootBreak, blockLength, confStop. These correspond to:

See Also

setForBoot, wBT

Examples

eList <- EGRET::Choptank_eList

# Completely interactive:
# caseSetUp <- trendSetUp(eList)
# Semi-interactive:
# caseSetUp <- trendSetUp(eList, nBoot = 100, blockLength = 200)

# fully scripted:
caseSetUp <- trendSetUp(eList,
  year1=1985, 
  year2=2005,
  nBoot = 50, 
  bootBreak = 39,
  blockLength = 200)

Run the WBT (WRTDS Bootstrap Test)

Description

Runs the WBT for a given data set to evaluate the significance level and confidence intervals for the trends between two specified years. The trends evaluated are trends in flow normalized concentration and flow normalized flux. Function produces text outputs and a named list (eBoot) that contains all of the relevant outputs. Check out runPairsBoot and runGroupsBoot for more bootstrapping options. The wBT only runs stationary flow normalization (i.e. making the assumption that discharge is stationary). The runPairsBoot and runGroupsBoot allow for generalized flow normalization (i.e. non-stationary discharge).

Usage

wBT(eList, caseSetUp, saveOutput = TRUE, fileName = "temp.txt",
  startSeed = 494817, jitterOn = FALSE, V = 0.2)

Arguments

Details

In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.

Value

eBoot, a named list with bootOut, wordsOut, xConc, xFlux, pConc, pFlux values.

See Also

trendSetUp, setForBoot, runGroupsBoot, runPairsBoot

Examples

eList <- EGRET::Choptank_eList
caseSetUp <- trendSetUp(eList,
                        year1 = 1985, 
                        year2 = 2005,
                        nBoot = 50, 
                        bootBreak = 39,
                        blockLength = 200)
# Very long-running function:                     
## Not run: 
eBoot <- wBT(eList,caseSetUp)

## End(Not run)