,
Robert Hirsch [aut] ,
Stacey Archfield [ctb] ,
Jennifer Murphy [ctb] | Title: | Exploration and Graphics for RivEr Trends Confidence Intervals |
|---|---|
| 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>. |
| Authors: | Laura DeCicco [aut, cre] ,
Robert Hirsch [aut] ,
Stacey Archfield [ctb] ,
Jennifer Murphy [ctb] |
| Maintainer: | Laura DeCicco <[email protected]> |
| License: | CC0 |
| Version: | 2.0.5 |
| Built: | 2024-11-19 05:57:28 UTC |
| Source: | https://github.com/doi-usgs/egretci |
| Package: | EGRETci |
| Type: | Package |
| License: | Unlimited for this package, dependencies have more restrictive licensing. |
| 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 https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits |
| LazyLoad: | yes |
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.
Robert M. Hirsch [email protected], Laura De Cicco [email protected]
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. https://www.sciencedirect.com/science/article/pii/S1364815215300220
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.
blockSample(localSample, blockLength, startSeed = NA)blockSample(localSample, blockLength, startSeed = NA)
localSample |
Sample data frame |
blockLength |
integer size of subset, expressed in days. 200 days has been found to be a good choice. |
startSeed |
setSeed value. This is used to make repeatable output. Default = NA. |
newSample data frame in same format as Sample data frame. It has the same number of rows as the Sample data frame.
library(EGRET) eList <- Choptank_eList Sample <- eList$Sample bsReturn <- blockSample(Sample, 200)library(EGRET) eList <- Choptank_eList Sample <- eList$Sample bsReturn <- blockSample(Sample, 200)
One bootstrap run used in calculating confidence interval bands.
bootAnnual(eList, blockLength = 200, startSeed = 494817, verbose = FALSE, jitterOn = FALSE, V = 0.2)bootAnnual(eList, blockLength = 200, startSeed = 494817, verbose = FALSE, jitterOn = FALSE, V = 0.2)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
blockLength |
integer default value is 200. |
startSeed |
setSeed value. Defaults to 494817. This is used to make repeatable output. |
verbose |
logical specifying whether or not to display progress message. |
jitterOn |
logical, if TRUE, adds "jitter" to the data in an attempt to avoid some numerical problems. Default = FALSE. See Details below. |
V |
numeric a multiplier for addition of jitter to the data, default = 0.2. |
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.
library(EGRET) eList <- Choptank_eList ## Not run: annualResults <- bootAnnual(eList) ## End(Not run)library(EGRET) eList <- Choptank_eList ## Not run: annualResults <- bootAnnual(eList) ## End(Not run)
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.
Computes confidence intervals for Flow-Normalized Concentration and Flow-Normalized Flux for a WRTDS model.
ciBands(eList, repAnnualResults, probs = c(0.05, 0.95))ciBands(eList, repAnnualResults, probs = c(0.05, 0.95))
eList |
named list with at least the Daily, Sample,
and INFO dataframes. Created from the EGRET package, after running
|
repAnnualResults |
named list returned from bootstrapping process. |
probs |
numeric vector low and high confidence interval frequencies, default = c(0.05, 0.95) (which results in a 90% confidence interval). |
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)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)
Function to calculate confidence bands for flow normalized concentration or
flow normalized flux.It returns the data frame CIAnnualResults, which is used
as input to the functions plotConcHistBoot, and
plotFluxHistBoot which produce the graphical output.
ciCalculations(eList, startSeed = 494817, verbose = TRUE, jitterOn = FALSE, V = 0.2, nBoot = 100, blockLength = 200, widthCI = 90)ciCalculations(eList, startSeed = 494817, verbose = TRUE, jitterOn = FALSE, V = 0.2, nBoot = 100, blockLength = 200, widthCI = 90)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
startSeed |
setSeed value. Defaults to 494817. This is used to make repeatable output. |
verbose |
logical specifying whether or not to display progress message, default = TRUE |
jitterOn |
logical, if TRUE, adds "jitter" to the data in an attempt to avoid some numerical problems. Default = FALSE. See Details below. |
V |
numeric a multiplier for addition of jitter to the data, default = 0.2. See Details below. |
nBoot |
number of times the bootstrap resampling and model estimating is done. Default is 100, but that will take a long time. Testing should initially be done using a smaller number like 10. |
blockLength |
integer size of subset, expressed in days. 200 days has been found to be a good choice. |
widthCI |
numeric, the width of the confidence intervals. 0.9 means the confidence intervals will be calculated with 90%. |
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% confidence interval)
CIAnnualResults a data frame with the following columns:
| Year | mean decYear value for the year being reported |
| FNConcLow | the lower confidence limit for flow normalized concentration, in mg/L |
| FNConcHigh | the upper confidence limit for flow normalized concentration, in mg/L |
| FNFluxLow | the lower confidence limit for flow normalized flux, in kg/day |
| FNFluxLow | the lower confidence limit for flow normalized flux, in kg/day |
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)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)
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.
genDailyBoot(eList, nBoot = 10, nKalman = 10, rho = 0.9, setSeed = NA, jitterOn = FALSE, V = 0.2)genDailyBoot(eList, nBoot = 10, nKalman = 10, rho = 0.9, setSeed = NA, jitterOn = FALSE, V = 0.2)
eList |
is the data with a fitted model already done. Note that the eList$Sample may have multiple values on a given day and it can also have censored values. |
nBoot |
number of times the bootstrap resampling and model estimating is done. |
nKalman |
number of different realizations of the daily time series for each re-estimated model. |
rho |
numeric the lag one autocorrelation. Default is 0.9. |
setSeed |
value. Defaults is |
jitterOn |
logical, if TRUE, adds "jitter" to the data in an attempt to avoid some numerical problems. Default = FALSE. See Details below. |
V |
numeric a multiplier for addition of jitter to the data, default = 0.2. See Details below. |
In some situations numerical problems are encountered in the bootstrap process, resulting in highly unreasonable spikes in the confidence intervals. The use of "jitter" can often prevent these problems, but should only be used when it is clearly needed. It adds a small amount of random "jitter" to the explanatory variables of the WRTDS model. The V parameter sets the scale of variation in the log discharge values. The standard deviation of the added jitter is V * standard deviation of Log Q. The default for V is 0.2. Larger values should generally be avoided, and smaller values may be sufficient.
dailyBootOut a matrix of daily flux values (in kg/day). The number of columns of the matrix is the number of replicates produced which is nBoot * nKalman The number of rows is the number of days in the record. The set of days simulated is the same set of days that are in the eList$Daily data frame.
eList <- EGRET::Choptank_eList # Very long running function: ## Not run: dailyBootOut <- genDailyBoot(eList, nBoot = 20, nKalman = 25) ## End(Not run)eList <- EGRET::Choptank_eList # Very long running function: ## Not run: dailyBootOut <- genDailyBoot(eList, nBoot = 20, nKalman = 25) ## End(Not run)
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.
makeAnnualPI(dailyBootOut, eList, paLong = 12, paStart = 10, fluxUnit = 3)makeAnnualPI(dailyBootOut, eList, paLong = 12, paStart = 10, fluxUnit = 3)
dailyBootOut |
data frame returned from |
eList |
named list with at least the Daily, Sample, and INFO
dataframes. Created from the EGRET package, after running |
paLong |
numeric integer specifying the length of the period of analysis, in months, 1<=paLong<=12, default is 12 |
paStart |
numeric integer specifying the starting month for the period of analysis, 1<=paStart<=12, default is 10 |
fluxUnit |
number representing entry in pre-defined fluxUnit class array.
|
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).
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"]])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"]])
This function takes the output from genDailyBoot and
calculates the quantiles for a daily aggregation.
makeDailyPI(dailyBootOut, eList, fluxUnit = 3)makeDailyPI(dailyBootOut, eList, fluxUnit = 3)
dailyBootOut |
data frame returned from |
eList |
named list with at least the Daily, Sample, and INFO
dataframes. Created from the EGRET package, after running |
fluxUnit |
number representing entry in pre-defined fluxUnit class array.
|
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).
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"]])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"]])
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.
makeMonthPI(dailyBootOut, eList, fluxUnit = 3)makeMonthPI(dailyBootOut, eList, fluxUnit = 3)
dailyBootOut |
data frame returned from |
eList |
named list with at least the Daily, Sample, and INFO
dataframes. Created from the EGRET package, after running |
fluxUnit |
number representing entry in pre-defined fluxUnit class array.
|
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).
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"]])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"]])
Convert a sequence of month integers into their decimal years.
monthSeqToDec(monthSeq)monthSeqToDec(monthSeq)
monthSeq |
integer vector of months. Month 1 is considered Jan. 1850. |
months <- 1558:1600 monthSeqToDec(months)months <- 1558:1600 monthSeqToDec(months)
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.
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, ...)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, ...)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
CIAnnualResults |
data frame generated from ciBands (includes nBoot, probs, and blockLength attributes). |
yearStart |
numeric is the calendar year containing the first estimated annual value to be plotted, default is NA (which allows it to be set automatically by the data). |
yearEnd |
numeric is the calendar year just after the last estimated annual value to be plotted, default is NA (which allows it to be set automatically by the data). |
plotFlowNorm |
logical variable if TRUE flow normalized concentration line is plotted, if FALSE not plotted, default is TRUE. |
col.pred |
character color of line for flow-normalized concentration and for the confidence limits, default is "green". |
concMax |
numeric specifying the maximum value to be used on the vertical axis, default is NA (which allows it to be set automatically by the data). |
plotAnnual |
logical variable if |
plotGenConc |
logical variable. If |
cex |
numeric value giving the amount by which plotting symbols should be magnified, default = 0.8. |
cex.axis |
numeric value of magnification to be used for axis annotation relative to the current setting of cex, default = 1.1. |
lwd |
numeric magnification of line width, default = 2. |
col |
color of annual mean points on plot, see ?par 'Color Specification', default = "black". |
col.gen |
color of annual mean points for WRTDS_K output on plot, see ?par 'Color Specification', default = "red". |
customPar |
logical defaults to FALSE. If TRUE, par() should be set by user before calling this function (for example, adjusting margins with par(mar=c(5,5,5,5))). If customPar FALSE, EGRETci chooses the best margins. |
printTitle |
logical print title of the plot, default = TRUE. |
cex.main |
numeric value of magnification to be used for plot title, default = 1.1. |
... |
graphical parameters |
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)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)
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.
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, ...)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, ...)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
CIAnnualResults |
data frame from ciBands (needs nBoot, probs, and blockLength attributes). |
yearStart |
numeric is the calendar year containing the first estimated annual value to be plotted, default is NA (which allows it to be set automatically by the data). |
yearEnd |
numeric is the calendar year just after the last estimated annual value to be plotted, default is NA (which allows it to be set automatically by the data). |
fluxUnit |
integer representing entry in pre-defined fluxUnit class array. |
fluxMax |
numeric specifying the maximum value to be used on the vertical axis, default is NA (which allows it to be set automatically by the data), uses units specificed by fluxUnit. |
plotFlowNorm |
logical variable if TRUE flow normalized flux line is plotted, if FALSE not plotted, default is TRUE. |
col.pred |
character color of line for flow-normalized flux and for the confidence limits, default is "green". |
plotAnnual |
logical variable if |
plotGenFlux |
logical variable. If |
cex |
numeric value giving the amount by which plotting symbols should be magnified, default = 0.8. |
cex.axis |
numeric magnification to be used for axis annotation relative to the current setting of cex, default = 1.1. |
lwd |
numeric magnification of line width, default = 2. |
col |
color of annual mean points on plot, see ?par 'Color Specification', default = "black". |
col.gen |
color of annual mean points for WRTDS_K output on plot, see ?par 'Color Specification', default = "red". |
cex.main |
numeric title scale |
printTitle |
logical print title of the plot, default = TRUE. |
customPar |
logical defaults to FALSE. If TRUE, par() should be set by user before calling this function (for example, adjusting margins with par(mar=c(5,5,5,5))). If customPar FALSE, EGRET chooses the best margins. |
... |
graphical parameters |
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)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)
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.
plotHistogramTrend(eList, eBoot, caseSetUp, 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", ...)plotHistogramTrend(eList, eBoot, caseSetUp, 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", ...)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
eBoot |
named list. Returned from |
caseSetUp |
data frame. Returned from |
flux |
logical if TRUE, plots flux results, if FALSE plots concentration results. |
xMin |
minimum bin value for histogram, it is good to have the xMin and xMax arguments straddle zero, default is NA (value set from the data). |
xMax |
maximum bin value for histogram, default is NA (value set from the data). |
xStep |
step size, typically multiples of 10 or 20, default is NA (value set from the data). |
printTitle |
logical if TRUE, plot includes title. |
cex.main |
numeric magnification of font size for title, default is 1.1. |
cex.axis |
numeric magnification of font size for axis, default is 1.1. |
cex.lab |
numeric magnification of font size for axis labels, default is 1.1. |
col.fill |
character fill color for histogram, default is "grey". |
... |
base R graphical parameters that can be passed to the hist function |
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.
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)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)
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.
pVal(s)pVal(s)
s |
numeric vector of slope values from the bootstrap |
pVal numeric value
s <- c(-1.0, 0, 0.5, 0.55, 3.0) pValue <- pVal(s)s <- c(-1.0, 0, 0.5, 0.55, 3.0) pValue <- pVal(s)
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.
runGroupsBoot(eList, groupResults, nBoot = 100, startSeed = 494817, blockLength = 200, jitterOn = FALSE, V = 0.2)runGroupsBoot(eList, groupResults, nBoot = 100, startSeed = 494817, blockLength = 200, jitterOn = FALSE, V = 0.2)
eList |
named list with at least the Daily, Sample, and INFO dataframes |
groupResults |
data frame returned from |
nBoot |
the maximum number of bootstrap replicates to be used, typically 100 |
startSeed |
setSeed value. Defaults to 494817. This is used to make repeatable output. |
blockLength |
integer size of subset, expressed in days. 200 days has been found to be a good choice. |
jitterOn |
logical, if TRUE, adds "jitter" to the data in an attempt to avoid some numerical problems. Default = FALSE. See Details below. |
V |
numeric a multiplier for addition of jitter to the data, default = 0.2. |
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.
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.
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) boot_group_out <- runGroupsBoot(eList, groupResults) plotHistogramTrend(eList, boot_group_out, caseSetUp=NA) ## End(Not run)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) boot_group_out <- runGroupsBoot(eList, groupResults) plotHistogramTrend(eList, boot_group_out, caseSetUp=NA) ## End(Not run)
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.
runPairsBoot(eList, pairResults, nBoot = 100, startSeed = 494817, blockLength = 200, jitterOn = FALSE, V = 0.2)runPairsBoot(eList, pairResults, nBoot = 100, startSeed = 494817, blockLength = 200, jitterOn = FALSE, V = 0.2)
eList |
named list with at least the Daily, Sample, and INFO dataframes |
pairResults |
data frame returned from |
nBoot |
the maximum number of bootstrap replicates to be used, typically 100 |
startSeed |
setSeed value. Defaults to 494817. This is used to make repeatable output. |
blockLength |
integer size of subset, expressed in days. 200 days has been found to be a good choice. |
jitterOn |
logical, if TRUE, adds "jitter" to the data in an attempt to avoid some numerical problems. Default = FALSE. See Details below. |
V |
numeric a multiplier for addition of jitter to the data, default = 0.2. |
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.
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.
library(EGRET) eList <- Choptank_eList year1 <- 1985 year2 <- 2009 ## Not run: pairOut_2 <- runPairs(eList, year1, year2, windowSide = 7) boot_pair_out <- runPairsBoot(eList, pairOut_2) plotHistogramTrend(eList, boot_pair_out, caseSetUp = NA) ## End(Not run)library(EGRET) eList <- Choptank_eList year1 <- 1985 year2 <- 2009 ## Not run: pairOut_2 <- runPairs(eList, year1, year2, windowSide = 7) boot_pair_out <- runPairsBoot(eList, pairOut_2) plotHistogramTrend(eList, boot_pair_out, caseSetUp = NA) ## End(Not run)
Saves critical information in a EGRETci workflow when analyzing trends between a starting and ending year.
saveEGRETci(eList, eBoot, caseSetUp, fileName = "")saveEGRETci(eList, eBoot, caseSetUp, fileName = "")
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
eBoot |
named list. Returned from |
caseSetUp |
data frame. Returned from |
fileName |
character. If left blank (empty quotes), the function will interactively ask for a name to save. |
A .RData file containing three objects: eList, eBoot, and caseSetUp
wBT, trendSetUp, modelEstimation
eList <- EGRET::Choptank_eList ## Not run: caseSetUp <- trendSetUp(eList) eBoot <- wBT(eList,caseSetUp) saveEGRETci(eList, eBoot, caseSetUp) ## End(Not run)eList <- EGRET::Choptank_eList ## Not run: caseSetUp <- trendSetUp(eList) eBoot <- wBT(eList,caseSetUp) saveEGRETci(eList, eBoot, caseSetUp) ## End(Not run)
Adds window parameters to INFO file in eList.
setForBoot(eList, caseSetUp, windowY = 7, windowQ = 2, windowS = 0.5, edgeAdjust = TRUE)setForBoot(eList, caseSetUp, windowY = 7, windowQ = 2, windowS = 0.5, edgeAdjust = TRUE)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
caseSetUp |
data frame returned from |
windowY |
numeric specifying the half-window width in the time dimension, in units of years, default is 7. |
windowQ |
numeric specifying the half-window width in the discharge dimension, units are natural log units, default is 2. |
windowS |
numeric specifying the half-window with in the seasonal dimension, in units of years, default is 0.5. |
edgeAdjust |
logical specifying whether to use the modified method for calculating the windows at the edge of the record, default is TRUE. |
eList list with Daily,Sample, INFO data frames and surface matrix.
eList <- EGRET::Choptank_eList caseSetUp <- trendSetUp(eList, year1=1985, year2=2005, nBoot = 50, bootBreak = 39, blockLength = 200) bootSetUp <- setForBoot(eList,caseSetUp)eList <- EGRET::Choptank_eList caseSetUp <- trendSetUp(eList, year1=1985, year2=2005, nBoot = 50, bootBreak = 39, blockLength = 200) bootSetUp <- setForBoot(eList,caseSetUp)
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.
trendSetUp(eList, ...)trendSetUp(eList, ...)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
... |
additional arguments to bring in to reduce interactive options (year1, year2, nBoot, bootBreak, blockLength) |
caseSetUp data frame with columns year1, yearData1, year2, yearData2, numSamples, nBoot, bootBreak, blockLength, confStop. These correspond to:
| Column Name | Manuscript Variable |
| year1 | |
| year2 | |
| nBoot | |
| bootBreak | |
| blockLength | |
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)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)
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).
wBT(eList, caseSetUp, saveOutput = TRUE, fileName = "temp.txt", startSeed = 494817, jitterOn = FALSE, V = 0.2)wBT(eList, caseSetUp, saveOutput = TRUE, fileName = "temp.txt", startSeed = 494817, jitterOn = FALSE, V = 0.2)
eList |
named list with at least the Daily, Sample, and INFO dataframes. Created from the EGRET package, after running |
caseSetUp |
data frame. Returned from |
saveOutput |
logical. If |
fileName |
character. Name to save the output file if |
startSeed |
setSeed value. Defaults to 494817. This is used to make repeatable output. |
jitterOn |
logical, if TRUE, adds "jitter" to the data in an attempt to avoid some numerical problems. Default = FALSE. See Details below. |
V |
numeric a multiplier for addition of jitter to the data, default = 0.2. See Details below. |
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.
eBoot, a named list with bootOut, wordsOut, xConc, xFlux, pConc, pFlux values.
| Object | Description |
| bootOut | a data frame with the results of the bootstrap test. |
| wordsOut | a character vector describing the results. |
| xConc and xFlux | 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 | 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. |
trendSetUp, setForBoot, runGroupsBoot, runPairsBoot
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)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)