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 |

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`

.

1 2 3 4 5 6 7 8 9 10 | ```
## 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
``` |

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