elementR_repSample: object elementR_repSample
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_repSample contains the main information needed for the filtration of a batch of replicates from the same sample.

1	elementR_repSample

An R6Class generator object

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

The elementR_repSample object inherits from the elementR_rep.

rep_type: A character string indicating the type of the considered batch (here, "sample")

rep_type2: A character string corresponding to the processing mode of averaging ("raster" or "spot")

rep_dataFiltre: A list containing the data to average of each replicate of the considered sample (dataOutlierFree for spot mode and dataNorm for raster mode)

rep_dataFinalSpot: A matrix containing the average and the standard deviation per chemical element of the dataOutlierFree of the final replicates (i.e. chosen to be part of the final calculation)

rep_dataIntermRaster: A list containing the realigned dataNorm of the final replicates (i.e. chosen to be part of the final calculation)

rep_dataFinalRaster: A matrix corresponding to the average values of the data contained in rep_dataIntermRaster

rep_autoCorrel: a vector which contains (1) laser diameter, (2) laser speed, (3) which point to keep

rep_dataFinalRasterNonCorr: a matrix of the final data without correlated points

setrep_type2(x): Aim: set rep_type2; Arguments: x = a character string indicating spot or raster mode

Realign2(data, pas): Aim: Realign data; Arguments: data = a list of matrix corresponding to the data to realign, pas = the step of time between two consecutive analysis within data of the considered sample; Output: data = a list of matrix containing the realigned data

setRep_dataFiltre(x): Aim: set rep_dataFiltre; Arguments: x = a logical value corresponding to the choice of user to correct or not the machine drift

setRep_dataFinalSpot(x): Aim: set rep_dataFinalSpot; Arguments: x = the matrix to set

intermStepSpot(): Aim: create and return an intermediate matrix containing the average and the standard deviation per chemical element for all sample replicates; Output: outputTab = a matrix with two lines corresponding to the average and the standard deviation per chemical element for all sample replicates

intermStepRaster(decalage, input, outliers, replace): Aim: create and return an intermediate matrix containing realigned data for all sample replicates; Inputs: decalage = a vector indicating the translation to operate, input = the data to realign, outliers = a list of outliers, replace = the value to replace in case of outlier, Output: outputList = a list of matrix containing realigned data for all sample replicates

setRep_dataIntermRaster(x): Aim: set setRep_dataIntermRaster; Arguments: x = the list of matrix to set

setRep_dataFinalRaster(): Aim: set rep_dataFinalRaster

create(): Aim: create and set the field rep_data by filling it with the elementR_sample objects corresponding to sample replicates included in this batch

set_rep_autoCorrel(x): Aim: set rep_autoCorrel, Input: x = the value to set

set_rep_dataFinalRasterNonCorr(): Aim: set rep_dataFinalRasterNonCorr

RealignCol(dat1, dat2, col, step): Aim: realign two tables according to a chosen column (based on a convolution); Inputs: dat1 & dat2 = matrix to realign, col = the column to realign, step = the step between two consecutive analysis; Outputs: the realign data

RealignColList(listRealig, col, step): Aim: realign a list of matrix according to a chosen column (based on a convolution); Inputs: listRealig = a list of matrix to realign, col = the column to realign, step = the step between two consecutive analysis; Outputs: the realign data

RealignAll(dat1, dat2, step): Aim: realign a list of matrix according to all columns (based on a convolution); Inputs: dat1 & dat2 = matrix to realign, step = the step between two consecutive analysis; Outputs: the realign data

RealignListAll(listRealig, step): Aim: realign a list of matrix according to all columns (based on a convolution); Inputs: listRealig = a list of matrix to realign, step = the step between two consecutive analysis; Outputs: the realign data

elementR_rep. elementR_repStandard.

## create a new elementR_sample object based on the "filePath" 
## from a folder containing sample replicate

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

sampleBatch <- elementR_repSample$new(filePath)

## Display the data contained in this batch

sampleBatch$rep_data