elementR_project: Object elementR_project

Description Usage Format Details Fields Methods See Also Examples

Description

The R6Class object elementR_project contains all the information needed for running an elementR session

Usage

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Format

An R6Class generator object

Details

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.

Fields

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

Methods

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

See Also

elementR_rep. elementR_data.

Examples

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## 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

elementR documentation built on Sept. 2, 2020, 5:07 p.m.