elementR_sample: Object elementR_sample
In elementR: An Framework for Reducing Elemental LAICPMS Data from Solid Structures

Description Usage Format Details Inheritance Fields Methods See Also Examples

The R6Class object elementR_sample contains the main information needed for the filtration of a single sample replicate.

1	elementR_sample

An R6Class generator object

As a subclass object, the elementR_sample object already contains the whole fields and methods from the elementR_data. Moreover, it also contains items specifically designed for sample filtration.

The elementR_sample object inherits from the elementR_data

type: A character string corresponding to the type of replicate (here, "sample")

dataConc: A matrix corresponding to the dataNorm converted in concentration

dataConcCorr: A matrix corresponding to the dataConc corrected (or not) from the machine drift

setDataConc(bins, plat, calibFile, meanStand, rempl): Aim: set dataConc; Arguments: bins = a numerical value corresponding to the time at which end the blank values, plat = a vector of two numerical values corresponding respectively to the time at which begin and end the plateau, calibFile = a matrix corresponding to the data of the calibration file, meanStand = a vector containing the averaged signal intensity per chemical element for all standard replicates of the running session, rempl = the value replacing data if below the limit of detection

setDataConcCorr(bins, plat, name, calibFile, meanStand, rankSample, rankStandard, model, correction, rempl, threshold): Aim: set dataConcCorr; Arguments: bins = a numerical value corresponding to the time at which end the blank values, plat = a vector of two numerical values corresponding respectively to the time at which begin and end the plateau, name = a character string corresponding to the name of the sample replicates, calibFile = a matrix corresponding to the the calibration file, meanStand = a vector containing the averaged signal intensity per chemical element for all standard replicates of the running session, rankSample = a vector containing the rank of each sample in ICPMS analysis, rankStandard = a vector containing the rank of each standard in ICPMS analysis, correction = a vector indicating the chemical elements to correct from machine drift, model = a matrix containing the parameters of the linear regression corresponding to the machine drift for all chemical elements, threshold = the R2 threshold to consider that the model does not fit to a linear model

renderData(curve): Aim: render data without proceding to their calculation; Argument: curve = a character string corresponding to the type of data to render ("Blank" for calculate and/or render the dataBlank, "Raw" for data, "Plateau" for dataPlateau, "Blank removed" for dataSuppBlank, ">LOD" for dataSupLOD, "Normalized" for dataNorm, "Concentration" for dataConc and "Conc. corrected" for dataConcCorr); Output: a matrix of the required data

getData(curve, bins, plat, name, calibFile, meanStand, rankSample, rankStandard, model, correction): Aim: calculate and render the required data ; Arguments: curve = a character string corresponding to the type of data to calculate (for more details, see renderData arguments), bins = a numerical value corresponding to the time at which end the blank values, plat = a vector of two numerical values corresponding respectively to the time at which begin and end the plateau, name = a character string corresponding to the name of the sample replicates, calibFile = a matrix corresponding to the the calibration file, meanStand = a vector containing the averaged signal intensity per chemical element for all standard replicates of the running session, rankSample = a vector containing the rank of each sample in ICPMS analysis, rankStandard = a vector containing the rank of each standard in ICPMS analysis, correction = a vector indicating the chemical elements to correct from machine drift, model = a matrix containing the parameters of the linear regression corresponding to the machine drift for all chemical elements, threshold = the R2 threshold to consider that the model does not fit to a linear model

elementR_data. elementR_standard.

## create a new elementR_sample object based on the "filePath" from a file containing data
## replicate (accepted format of data: .csv, .ods, .xls, .xlsx)

filePath <- system.file("Example_Session/samples/Sample_1/Sample1_Rep1.csv", package="elementR")

sampleExample <- elementR_sample$new(filePath)

## Display the name of the object 

sampleExample$name