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

Description Usage Format Details Fields Methods See Also Examples

The R6Class object elementR_data contains the main information needed for the filtration of a single replicate (sample or standard).

1	elementR_data

An R6Class generator object

When runElementR is running and as soon as a project is loaded, an elementR_data is automatically created for each replicate included in the session (standard and sample). Each of these objects contains the basic information regarding the considered replicate (name, path and raw data) and is filled by the intermediate and final data as user proceeds to the filtration procedure.

name: A character string corresponding to the name of the considered replicate

data: A matrix corresponding to the raw data of the considered replicate

fPath: A character string corresponding the path of the raw data

bins: A numerical value corresponding to the time at which end the blank values

plat: A vector containing two numerical values corresponding respectively to the time at which begin and end the plateau values

dataBlank: A matrix corresponding to the blank data

dataPlateau: A matrix corresponding to the plateau data

dataSuppBlank: A matrix corresponding to the data obtained by substracting the averaged blank value (here, BlankAverarge) from the dataPlateau

dataSupLOD: A matrix of data corresponding to the values of dataSuppBlank up to the limit of detection (here LOD)

dataNorm: A matrix of data corresponding to the values of dataSupLOD normalized by the chemical element chosen as internal standard (here, elemstand)

elemstand: A character string corresponding to the name of the chemical element chosen as internal standard

LOD: A vector of numerical values corresponding to the limit of detection for each chemical element of the considered replicate

BlankAverarge: A vector of numerical values corresponding to the averaged blank value for each chemical element of the considered replicate

remplaceValue: A character string corresponding to the value replacing the dataSuppBlank below the limit of detection

initialize(filePath, sep , dec): Aim: Create and set basic information of the considered replicate; Argument: filePath = the path of the considered replicate data, dec = the decimal system of the data, sep = the separator character of the data; Output: an R6Class elementR_data object

setBins(bins): Aim: set bins; Argument: bins = A numerical value corresponding to the time at which end the blank values

setPlat(plat): Aim: set plat; Argument: plat = A vector containing two numerical values corresponding respectively to the time at which begin and end the plateau values

setDataBlanc(bins): Aim: set dataBlank; Argument: bins = A numerical value corresponding to the time at which end the blank values

setDataPlateau(plat): Aim: set dataPlateau; Argument: plat = A vector containing two numerical values corresponding respectively to the time at which begin and end the plateau values

setDataSuppBlank(bins,plat): Aim: set dataSuppBlank; Arguments: bins = A numerical value corresponding to the time at which end the blank values, plat = A vector containing two numerical values corresponding respectively to the time at which begin and end the plateau values

setDataSupLOD(bins,plat): Aim: set dataSupLOD; Arguments: bins = A numerical value corresponding to the time at which end the blank values, plat = A vector containing two numerical values corresponding respectively to the time at which begin and end the plateau values

setDataNorm(bins,plat): Aim: set dataNorm; Arguments: bins = A numerical value corresponding to the time at which end the blank values, plat = A vector containing two numerical values corresponding respectively to the time at which begin and end the plateau values

reset(): Aim: replace dataConcCorr by NA

OutlierDetectTietjen(x, nbOutliers): Aim: return the place of the outlier of a vector according to Tietjen and outlier methods; Arguments: x = a vector, nbOutliers = the number of suspected outliers; Outputs: a vector of the position of the outlier in the vector

outlierDetection(dat, method, nbOutliers): Aim: return the place of the outlier of a vector; Arguments: dat = a vector, method = the method used for the detection ("Tietjen.Moore Test", "SD criterion", "Rosner's test"), nbOutliers = the number of suspected outliers; Outputs: a vector of the position of the outlier in the vector

detectOutlierMatrix(dat, method, nbOutliers): Aim: return the place of the outlier for each column of a matrix; Arguments: dat = a matrix, method = the method used for the detection ("Tietjen.Moore Test", "SD criterion", "Rosner's test"), nbOutliers = the number of suspected outliers; Outputs: a list of vector corresponding to the position of the outlier in each column of the matrix

outlierReplace(dat, outlierList, rempl): Aim: replace the outliers value of a matrix by rempl; Arguments: dat = a matrix, a list showing the place of the outlier for each column, rempl: the value to replace if outliers

is.possibleOutlier(dat): Aim: check that the vector fits with the needs for outlier detection (length of data > 30 and not all the same); Arguments: dat = a vector of data; OUtputs: TRUE: the investigated vector meets the conditions, FALSE: the investigated vector does not meet the conditions

elementR_sample. elementR_standard.

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

filePath <- system.file("Example_Session/standards/Stand3.xls", package="elementR")

standard <- elementR_data$new(filePath)

## Display the raw data 

standard$data