Description Usage Format Details Fields Methods See Also Examples
The R6Class
object elementR_project
contains all the information needed for running an elementR session
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An R6Class
generator object
The elementR_project
structure allows to organized data in a session framework, faciliting therefore numerous major functionalities: handling as many standard replicates as wanted, machine drift verification and correction, sample replicate realignment and averaging. Moreover, this object can be easily exported, allowing user to re-open it later for finishing or editing final results.
name
A character string corresponding to the name of the project
folderPath
A character string corresponding to the path of the project
standardsPath
A character string corresponding to the path of the standard folder
standardsFiles
A vector containing the names of each standard file
standards
A list containing the elementR_repStandard
of each type of standard
samplesPath
A character string corresponding to the path of the sample folder
samplesFiles
A vector containing the names of each sample file
samples
A list containing the elementR_repSample
of each sample
EtalonPath
A character string corresponding to the path of the calibration file
EtalonData
A matrix corresponding to the calibration data
listeElem
A vector containing the names of the chemical elements included in the project
flag_stand
A vector indicating which standards have been filtered
flag_Sample
A vector indicating which samples have been filtered
flagRealign
A list vectors indicating which samples have been realigned or averaged
standardRank
A vector corresponding to the standard rank in ICPMS analysis
sampleRank
A vector corresponding to the sample rank in ICPMS analysis
elementChecking
A list indicating the number and the location of the error(s) of structure within data included in the project
errorSession
A numerical value indicating the non numeric error(s) within data included in the project
regressionModel
A matrix summarizing, for each chemical element, the parameters of the linear regression corresponding to the machine drift
machineCorrection
A vector summarizing the chemical element(s) to correct from machine drift
flagMachineCorrection
A numerical value indicating the validation of the machine correction step
nbCalib
A vector corresponding to the number of standard values available for each chemical element to proceed the linear regression
elemStand
A character string indicating the chemical element considered as internal standard (by default = Ca)
summarySettings
A matrix summarizing all the parameters set by user for each replicate (sample and standard)
ChoiceUserCorr
A logical value corresponding to the choice of the user to correct or no the session based on the first step of configuration
R2Threshold
the threshold to switch the machine drift correction from a linear to a neighbor correction
set_summarySettings(name, rank, bins, plat1, plat2, average, LOD)
Aim: set summarySettings
; Arguments: name = a character string corresponding to the name of the replicate to set, rank= its rank in ICPMS analysis, bins = a numerical value corresponding to the time at which end the blank values, plat1 = a numerical value corresponding to the time at which begin the plateau values, plat2 = a numerical value corresponding to the time at which end the plateau values, average = a vector corresponding to the blank averaged value (here, BlankAverarge
) for each chemical element of the considered replicate, LOD = a vector corresponding to the limit of detection (here, LOD
) for each chemical element of the considered replicate
is.integer0(x)
Aim: test the integer(0); Arguments: x = a vector to test; Outputs: TRUE or FALSE
closest(x,y)
Aim: find the nearest value among a vector of numerical data; Arguments: x = a vector of numerical values, y = the investigated value; Output: val = a list of two values: the nearest value and its place within the vector
PlotIC(name, Mean,SD, coord, lengthSeg, xlim, ylim, type = "p", xlab, ylab)
Aim: plot mean +/- SD; Arguments: name = a vector of the names to display on xaxis, Mean = a vector of mean, SD = a vector of SD, coord = a vector of coordonnates to place xticks, lengthSeg = a numeric value cooresponding to the length of the top segment of the SD bar, xlim & ylim = the limits of plots, xlab & ylab = the labels of axis
setEtalon(x, sep, dec)
Aim: define EtalonPath
and EtalonData
and check the validity of their data structure; Arguments: x = a character string corresponding to the path of the calibration file, dec = the decimal system of the data, sep = the separator character of the data
setflagMachineCorrection(x)
Aim: set flagMachineCorrection
; Arguments: x = the numerical value to set
NonNumericCheck(data, col)
Aim: check non numeric characters of data; Arguments: data = a dataframe or a matrix, col = a vector of numerical values corresponding to the column(s) to investigate; Output: errB = a numerical value corresponding to the number of cells containing non numeric characters
setflagStand(place, value)
Aim: set flag_stand
; Arguments: place = a numerical value corresponding to the considered replicate, value = the numerical value to set
setflagSample(sample, replicate, value)
Aim: set flag_Sample
; Arguments: sample = a numerical value corresponding to the considered sample, replicate = a numerical value corresponding to the considered replicate, value = the numerical value to set
setCorrection(x)
Aim: set machineCorrection
; Arguments: x = a vector indicating the chemical elements to correct from machine drift
correction()
Aim: proceed to the linear regression on standards replicates and set nbCalib
& regressionModel
setRank(type, value)
Aim: set the order in which ICPMS runs each standard (standardRank
) and sample (sampleRank
) replicates; Arguments: type = a character string indicating the type of replicate standard ("standard") or sample ("sample"), value = a numerical value corresponding to the rank of the considered replicate
set_flagRealign(replicate, type, value)
Aim: set flagRealign
; Arguments: replicate = a numerical value corresponding to the number of the considered replicate, type = a character string indicating the raster or spot mode, value = the numerical value to set
setElemStand(elem)
Aim: define elemStand
and transmit this value to all elementR_rep
and elementR_data
objects inlcuded in the project; Arguments: elem = a character string corresponding to the element considered as intern standard
initialize(folderPath, sep, dec)
Aim: create the project; Arguments: filePath = the path of the considered project, dec = the decimal system of the data, sep = the separator character of the data; Outputs: R6Class elementR_project
set_ChoiceUserCorr(x)
Aim: information about the will of user to check or not the machine drift; Arguments: x = T (for checking machine drift), F (for not checking machine drift)
setR2Threshold(x)
Aim: set R2Threshold
; Arguments: x = a value between 0 and 1
insert.at(a, pos, toInsert)
Aim: insert values in vectors; Arguments: a = a vector, pos = the position to insert, toInsert = a vector to insert
detectPlateau(dat, col)
Aim: detection of the plateau limits of a matrix based on clustering methods and on the internal standard element; Arguments: dat = the data to proceed, col = the column used for the detection
detectBlank(dat, col)
Aim: detection of the blank limits of a matrix based on the derivative value and on the internal standard element; Arguments: dat = the data to proceed, col = the column used for the detection
1 2 3 4 5 6 7 8 9 10 | ## create a new elementR_repStandard object based on the "filePath"
## from a folder containing sample replicate
# filePath <- system.file("Example_Session", package="elementR")
# exampleProject <- elementR_project$new(filePath)
## Display the raw data
# exampleProject$samplesFiles
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