Title:	Visually Guided Preprocessing of Bioanalytical Laboratory Data
Version:	0.1.1
Maintainer:	Jorn Lotsch <j.lotsch@em.uni-frankfurt.de>
Description:	Reproducible cleaning of biomedical laboratory data using visualization, error correction, and transformation methods implemented as interactive R notebooks. A detailed description of the methods ca ben found in Malkusch, S., Hahnefeld, L., Gurke, R. and J. Lotsch. (2021) <doi:10.1002/psp4.12704>.
License:	GPL (≥ 3)
Encoding:	UTF-8
URL:	https://github.com/JornLotsch/pguIMP
BugReports:	https://github.com/JornLotsch/pguIMP/issues
Imports:	R6 (≥ 2.4.1), DT, DataVisualizations, dbscan, dplyr, e1071, finalfit, ggplot2, ggthemes, Hmisc, magrittr, MASS, RWeka, VIM, bbmle, grid, gridExtra, mice, nortest, outliers, plotly, psych, purrr, rcompanion, readr, readxl, rlang, rmarkdown, robust, shiny, stats, stringr, tibble, tidyr, tidyselect, tools, writexl
Collate:	'dLogLikelihood.R' 'importDataSet.R' 'normalDistribution.R' 'pguCorrValidator.R' 'pguCorrelator.R' 'pguDMwR.R' 'pguFile.R' 'pguData.R' 'pguFilter.R' 'pguLimitsOfQuantification.R' 'pguImporter.R' 'pguStatus.R' 'pguReporter.R' 'pguExporter.R' 'pguValidator.R' 'pguImputation.R' 'pguOutlierDetection.R' 'pguOutliers.R' 'pguMissingsCharacterizer.R' 'pguMissings.R' 'pguNormalizer.R' 'sLogLikelihood.R' 'pguNormDist.R' 'pguModel.R' 'pguTransformator.R' 'pguOptimizer.R' 'pguExplorer.R' 'pguDelegate.R' 'transposeTibble.R' 'pguIMP.R' 'pguRegressor.R'
Suggests:	knitr, devtools, ellipsis (≥ 0.3.2), roxygen2
NeedsCompilation:	no
RoxygenNote:	7.3.3
Packaged:	2026-02-18 06:23:40 UTC; joern
Author:	Sebastian Malkusch [aut], Jorn Lotsch [aut, cre]
Repository:	CRAN
Date/Publication:	2026-02-20 10:40:26 UTC

Visually Guided Preprocessing of Bioanalytical Laboratory Data

Description

Reproducible cleaning of biomedical laboratory data using visualization, error correction, and transformation methods implemented as interactive R notebooks. A detailed description of the methods ca ben found in Malkusch, S., Hahnefeld, L., Gurke, R. and J. Lotsch. (2021) <doi:10.1002/psp4.12704>.

Author(s)

Sebastian Malkusch [aut] (ORCID: <https://orcid.org/0000-0001-6766-140X>), Jorn Lotsch [aut, cre] (ORCID: <https://orcid.org/0000-0002-5818-6958>)

Maintainer: Jorn Lotsch <j.lotsch@em.uni-frankfurt.de>

References

Malkusch S, Hahnefeld L, Gurke R, Lotsch J (2021). "Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)." CPT: Pharmacometrics and Systems Pharmacology, 10(11), 1371–1381. doi:10.1002/psp4.12704

Examples

# simple examples of the most important functions

centralValue

Description

Returns the central value of a variable.

Usage

centralValue(x, ws = NULL)

Arguments

Details

Function that obtains a statistic of centrality of a variable, given a sample of values. If the variable is numeric it returns de median, if it is a factor it returns the mode. In other cases it tries to convert to a factor and then returns the mode. Taken from: https://github.com/ltorgo/DMwR2/

Value

central value

Author(s)

Luis Torgo

Examples

centralValue(x = seq(1,10,1))

dLogLikelihood

Description

Calculates the log Likelihood of a normally distributed event.

Usage

dLogLikelihood(x = "numeric", pars = c(mu = 0, sigma = 1))

Arguments

Value

The logLikelihood. (numeric)

Author(s)

Sebastian Malkusch

Examples

y <- pguIMP::dLogLikelihood(x=5, pars = c(mu=0.0, sigma=1.0))

importDataSet

Description

Imports a data set to the shiny 'pguIMP' web interface. Extracts import options from a 'pguIMP::file' instance and imports the desired record based on the passed information.

Usage

importDataSet(obj = "pgu.file")

Arguments

Value

A data frame that contains the imported data (tibble::tibble)

Author(s)

Sebastian Malkusch

knnImputation

Description

Imputes missings using kNN.

Usage

knnImputation(data, k = 10, scale = TRUE, meth = "weighAvg", distData = NULL)

Arguments

Details

Function that fills in all unknowns using the k Nearest Neighbours of each case with unknows. By default it uses the values of the neighbours and obtains an weighted (by the distance to the case) average of their values to fill in the unknows. If meth='median' it uses the median/most frequent value, instead. Taken from https://github.com/ltorgo/DMwR2/

Value

cleaned data

Author(s)

Luis Torgo

Examples

centralValue(x = seq(1,10,1))

nnk

Description

Outlier detection using kth Nearest Neighbour Distance method Takes a dataset and finds its outliers using distance-based method

Usage

nnk(
  x,
  k = 0.05 * nrow(x),
  cutoff = 0.95,
  Method = "euclidean",
  rnames = FALSE,
  boottimes = 100
)

Arguments

Details

nnk computes kth nearest neighbour distance of an observation and based on the bootstrapped cutoff, labels an observation as outlier. Outlierliness of the labelled 'Outlier' is also reported and it is the bootstrap estimate of probability of the observation being an outlier. For bivariate data, it also shows the scatterplot of the data with labelled outliers.

Value

Outlier Observations: A matrix of outlier observations

Location of Outlier: Vector of Sr. no. of outliers

Outlier probability: Vector of proportion of times an outlier exceeds local bootstrap cutof

Author(s)

Vinay Tiwari, Akanksha Kashikar

References

Hautamaki, V., Karkkainen, I., and Franti, P. 2004. Outlier detection using k-nearest neighbour graph. In Proc. IEEE Int. Conf. on Pattern Recognition (ICPR), Cambridge, UK.

Examples

#Create dataset
X=iris[,1:4]
#Outlier detection
nnk(X,k=4)

normalDistribution

Description

Probability density distribution of a normally distributed variable.

Usage

normalDistribution(x = "numeric", mu = "numeric", sigma = "numeric")

Arguments

Details

Calculates p(x | mu, sigma). Where p is the probability of observing an event x given the expected value mu and the standard deviation sigma.

Value

The probability of observing event x given mu and sigma. (numeric)

Author(s)

Sebastian Malkusch

Examples

y <- pguIMP::normalDistribution(x=5, mu=0.0, sigma=1.0)

pgu.corrValidator

Description

An R6 class that performs pairwise correlation of the features of the original and the imputed data set. The correlation results of both data sets are compared by subtraction.

Format

[R6::R6Class] object.

Construction

x <- pguIMP::pgu.corrValidator$new()

Active bindings

Methods

Public methods

• pgu.corrValidator$new()

• pgu.corrValidator$print()

• pgu.corrValidator$reset()

• pgu.corrValidator$fit()

• pgu.corrValidator$correlationScatterPlot()

• pgu.corrValidator$correlationBarPlot()

• pgu.corrValidator$correlationBoxPlot()

• pgu.corrValidator$correlationCompoundPlot()

• pgu.corrValidator$clone()

Method new()

Clears the heap and indicates if instance of 'pgu.corrValidator' is removed from heap.

Summary of the correlation deviation distribution.

Creates a square matrix which dimension corresponds to the length of the instance variable featureNames. The matrix entries are set to a distinct 'value'.

Flattens the results transforms them into a dataframe and stores it into the instance variable corr_df.

Creates and returns a new 'pgu.corrValidator' object.

Usage

pgu.corrValidator$new(org_df = "tbl_df", imp_df = "tbl_df")

Arguments

Returns

A new 'pgu.corrValidator' object. (pguIMP::pgu.corrValidator)

Method print()

Prints instance variables of a 'pgu.corrValidator' object.

Usage

pgu.corrValidator$print()

Returns

string

Method reset()

Resets the object 'pgu.corrValidator' based on the instance variable featureNames..

Usage

pgu.corrValidator$reset()

Method fit()

Runs the corraltion analysis.

Usage

pgu.corrValidator$fit(org_df = "tbl_df", imp_df = "tbl_df")

Arguments

Method correlationScatterPlot()

Plots the correlation analysis results.

Usage

pgu.corrValidator$correlationScatterPlot()

Method correlationBarPlot()

Creates and returns a histogram from the cor_delat values.

Usage

pgu.corrValidator$correlationBarPlot()

Returns

Bar plot (ggplot2::ggplot)

Method correlationBoxPlot()

Plots the correlation analysis results.

Usage

pgu.corrValidator$correlationBoxPlot()

Method correlationCompoundPlot()

Creates and returns a compund graphical analysis of the cor_delta values.

Usage

pgu.corrValidator$correlationCompoundPlot()

Returns

Compound plot (gridExtra::grid.arrange)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.corrValidator$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

Examples

require(dplyr)
require(tibble)
data(iris)
data_df <- iris %>%
  tibble::as_tibble()
comp_df <- data_df %>%
  dplyr::mutate(Sepal.Length = sample(Sepal.Length))
corr_obj = pguIMP::pgu.corrValidator$new()
corr_obj$fit(data_df, comp_df)
print(corr_obj)

pgu.correlator

Description

An R6 class that performs pairwise correlation on the pguIMP data set.

Format

[R6::R6Class] object.

Construction

x <- pguIMP::pgu.correlator$new()

Active bindings

Methods

Public methods

• pgu.correlator$new()

• pgu.correlator$finalize()

• pgu.correlator$print()

• pgu.correlator$resetCorrelator()

• pgu.correlator$resetMatrix()

• pgu.correlator$featureIdx()

• pgu.correlator$calcCorrelationNumeric()

• pgu.correlator$createCorrelationMatrixPearson()

• pgu.correlator$createCorrelationMatrixKendall()

• pgu.correlator$createCorrelationMatrixSpearman()

• pgu.correlator$correlate()

• pgu.correlator$printFeature()

• pgu.correlator$printRTbl()

• pgu.correlator$printPPearsonTbl()

• pgu.correlator$printTauTbl()

• pgu.correlator$printPKendallTbl()

• pgu.correlator$printRhoTbl()

• pgu.correlator$printPSpearmanTbl()

• pgu.correlator$clone()

Method new()

Creates and returns a new 'pgu.correlator' object.

Usage

pgu.correlator$new(data = "tbl_df")

Arguments

Returns

A new 'pgu.correlator' object. (pguIMP::pgu.correlator)

Method finalize()

Clears the heap and indicates if instance of 'pgu.correlator' is removed from heap.

Usage

pgu.correlator$finalize()

Method print()

Prints instance variables of a 'pgu.correlator' object.

Usage

pgu.correlator$print()

Returns

string

Method resetCorrelator()

Performes pair-wise correlation analysis on the attributes of the data frame. Progresse is indicated by the progress object passed to the function.

Usage

pgu.correlator$resetCorrelator(data = "tbl_df", progress = "Progress")

Arguments

Method resetMatrix()

Creates a square matrix which dimension corresponds to the length of the instance variable featureNames. The matrix entries are set to a distict 'value'.

Usage

pgu.correlator$resetMatrix(value = "numeric")

Arguments

Returns

A square matrix. (matrix)

Method featureIdx()

Determines the numerical index of the column of an attribute based on the attribute name.

Usage

pgu.correlator$featureIdx(feature = "character")

Arguments

Returns

The attributes column index. (numeric)

Method calcCorrelationNumeric()

Creates a correlation test between two attributes of a dataframe. The test is stored as instance variable.

Usage

pgu.correlator$calcCorrelationNumeric(
  abscissa = "numeric",
  ordinate = "numeric",
  method = "character"
)

Arguments

Method createCorrelationMatrixPearson()

Performs the actual correlation test routine after Pearson. Iteratively runs through the attributes known to the class and calculates Pearson's correlation for each valid attribute pair. The test results are stored in the instance variables: r, pPearson. Here, pX represents the p-value of the respective parameter X. Displays the progress if shiny is loaded.

Usage

pgu.correlator$createCorrelationMatrixPearson(
  data = "tbl_df",
  progress = "Progress"
)

Arguments

Method createCorrelationMatrixKendall()

Performs the actual correlation test routine after Kendall. Iteratively runs through the attributes known to the class and calculates Kendall's correlation for each valid attribute pair. The test results are stored in the instance variables: tau, pKendall. Here, pX represents the p-value of the respective parameter X. Displays the progress if shiny is loaded.

Usage

pgu.correlator$createCorrelationMatrixKendall(
  data = "tbl_df",
  progress = "Progress"
)

Arguments

Method createCorrelationMatrixSpearman()

Performs the actual correlation test routine after Spearman. Iteratively runs through the attributes known to the class and calculates Spearman's correlation for each valid attribute pair. The test results are stored in the instance variables: rho, pSpearman. Here, pX represents the p-value of the respective parameter X. Displays the progress if shiny is loaded.

Usage

pgu.correlator$createCorrelationMatrixSpearman(
  data = "tbl_df",
  progress = "Progress"
)

Arguments

Method correlate()

Performs the all three correlation test routines defined within the instance variable 'method'. Displays the progress if shiny is loaded.

Usage

pgu.correlator$correlate(data = "tbl_df", progress = "Progress")

Arguments

Method printFeature()

Transforms the results of the correlation procedure for a valid pair of attributes to a dataframe and returns it.

Usage

pgu.correlator$printFeature()

Returns

The analyis result as a dataframe. (tibble::tibble)

Method printRTbl()

Transfroms instance variable 'r' to a dataframe and returns it.

Usage

pgu.correlator$printRTbl()

Returns

Dataframe of instance variable 'r'. (tibble::tibble)

Method printPPearsonTbl()

Transfroms instance variable 'pPearson' to a dataframe and returns it.

Usage

pgu.correlator$printPPearsonTbl()

Returns

Dataframe of instance variable 'pPearson'. (tibble::tibble)

Method printTauTbl()

Transfroms instance variable 'tau' to a dataframe and returns it.

Usage

pgu.correlator$printTauTbl()

Returns

Dataframe of instance variable 'tau'. (tibble::tibble)

Method printPKendallTbl()

Transfroms instance variable 'pKendall' to a dataframe and returns it.

Usage

pgu.correlator$printPKendallTbl()

Returns

Dataframe of instance variable 'pKendall'. (tibble::tibble)

Method printRhoTbl()

Transfroms instance variable 'rho' to a dataframe and returns it.

Usage

pgu.correlator$printRhoTbl()

Returns

Dataframe of instance variable 'rho'. (tibble::tibble)

Method printPSpearmanTbl()

Transfroms instance variable 'pSpearman' to a dataframe and returns it.

Usage

pgu.correlator$printPSpearmanTbl()

Returns

Dataframe of instance variable 'pSpearman'. (tibble::tibble)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.correlator$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

Examples

require(dplyr)
require(tibble)
data(iris)
data_df <- iris %>%
  tibble::as_tibble() %>%
  dplyr::select(-c("Species"))
correlator = pguIMP::pgu.correlator$new(data_df)

pgu.data

Description

Handles the pguIMP dataset.

Format

[R6::R6Class] object.

Details

Stores the pguIMP dataset as instance variable and keeps track of the attributes of interest. Provides additionally fast access to several statistical information about the data set. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.data$new()

• pgu.data$print()

• pgu.data$fit()

• pgu.data$attribute_index()

• pgu.data$numerical_data()

• pgu.data$categorical_data()

• pgu.data$clone()

Method new()

Clears the heap and indicates that instance of pguIMP::pgu.data is removed from heap.

Summarizes information on the instance variable rawData and retruns it in form of a compact data frame.

Summarizes a vector of numericals and returns summary.

Iterativley calls the function summarize_numerical_data on all numerical attributes of the instance variable rawData and returns the result in form of a data frame.

Calls the function calculate_statistics filters the result for the attribute names and arithmetic mean values. and returns the result in form of a data frame.

Calls the class' function dataStatistics filters the result for the attribute names and information about missing values. and returns the result in form of a data frame.

Creates and returns a new pguIMP::pgu.data object.

Usage

pgu.data$new(data_df = "tbl_df")

Arguments

Returns

A new pguIMP::pgu.data object. (pguIMP::pgu.data)

Method print()

Prints instance variables of a pguIMP::pgu.data object.

Usage

pgu.data$print()

Returns

string

Method fit()

Extracts information about the instance variable rawData.

Usage

pgu.data$fit()

Method attribute_index()

Returns the index of an attribute within the instance variable attributeNames.

Usage

pgu.data$attribute_index(attribute = "character")

Arguments

Returns

Index of attribute's name in rawData (numeric)

Method numerical_data()

Returns the numeric attributes of the instance variable rawData.

Usage

pgu.data$numerical_data()

Returns

A data frame (tibble::tibble)

Method categorical_data()

Returns the categorical attributes of the instance variable rawData.

Usage

pgu.data$categorical_data()

Returns

A data frame (tibble::tibble)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.data$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

Examples

require(dplyr)
require(tibble)
data(iris)
data_df <- iris %>%
  tibble::as_tibble()
data_obj = pguIMP::pgu.data$new(data_df)

pgu.delegate

Description

Manages the communication between the shiny gui layer and the classes of the pguIMP package

Format

R6::R6Class object.

Details

Comprises all needed classes from the pguIMP package and manages the communication between the gui and the analysis. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.delegate$new()

• pgu.delegate$print()

• pgu.delegate$update_import_gui()

• pgu.delegate$query_data()

• pgu.delegate$import_data()

• pgu.delegate$update_import_data_Types_tbl()

• pgu.delegate$update_import_data_statistics_tbl()

• pgu.delegate$update_import_missings_statistics_tbl()

• pgu.delegate$update_filter_select_tbl()

• pgu.delegate$update_filter()

• pgu.delegate$update_filter_inverse()

• pgu.delegate$reset_filter()

• pgu.delegate$filter_data()

• pgu.delegate$update_filter_statistics_tbl()

• pgu.delegate$update_filter_missings_tbl()

• pgu.delegate$update_exploration_gui()

• pgu.delegate$update_exploration_abscissa()

• pgu.delegate$update_exploration_ordinate()

• pgu.delegate$update_exploration_graphic()

• pgu.delegate$update_exploration_abscissa_graphic()

• pgu.delegate$update_exploration_ordinate_graphic()

• pgu.delegate$update_exploration_abscissa_table()

• pgu.delegate$update_exploration_ordinate_table()

• pgu.delegate$reset_loq_values()

• pgu.delegate$update_loq_upload_gui()

• pgu.delegate$query_loq()

• pgu.delegate$import_loq()

• pgu.delegate$update_loq_define_gui()

• pgu.delegate$update_loq_define_feature()

• pgu.delegate$update_loq_define_lloq()

• pgu.delegate$update_loq_define_uloq()

• pgu.delegate$update_loq_define_table()

• pgu.delegate$update_loq_define_menu()

• pgu.delegate$set_loq_define_values()

• pgu.delegate$set_loq_define_values_globally()

• pgu.delegate$update_loq_detect_gui()

• pgu.delegate$update_loq_na_handling()

• pgu.delegate$init_detect_loq()

• pgu.delegate$detect_loq()

• pgu.delegate$update_loq_detect_statistics_tbl()

• pgu.delegate$update_loq_detect_outlier_tbl()

• pgu.delegate$update_loq_detect_statistics_graphic()

• pgu.delegate$update_loq_detect_attribute_graphic()

• pgu.delegate$update_loq_detect_attribute_tbl()

• pgu.delegate$update_loq_mutate_gui()

• pgu.delegate$update_lloq_substitute()

• pgu.delegate$update_uloq_substitute()

• pgu.delegate$init_mutate_loq()

• pgu.delegate$mutate_loq()

• pgu.delegate$update_loq_mutate_data_tbl()

• pgu.delegate$update_loq_mutate_statistics_graphic()

• pgu.delegate$update_loq_mutate_attribute_graphic()

• pgu.delegate$init_loq_mutate_attribute_tbl()

• pgu.delegate$update_loq_mutate_attribute_tbl()

• pgu.delegate$optimizeTrafoParameter()

• pgu.delegate$updateDetectedTrafoTypes()

• pgu.delegate$updateDetectedTrafoParameter()

• pgu.delegate$updateTrafoDetectGui()

• pgu.delegate$updateTrafoMutateFeature()

• pgu.delegate$updateTrafoMutateType()

• pgu.delegate$updateTrafoMutateLambda()

• pgu.delegate$updateTrafoMutateMirror()

• pgu.delegate$resetTrafoMutateGui()

• pgu.delegate$updateTrafoMutateGui()

• pgu.delegate$trafoMutateFit()

• pgu.delegate$trafoMutateGlobal()

• pgu.delegate$trafoMutateFeature()

• pgu.delegate$updateTrafoMutateFeatureGraphic()

• pgu.delegate$updateTrafoMutateFeatureParameterTbl()

• pgu.delegate$updateTrafoMutateFeatureQualityTbl()

• pgu.delegate$updateTrafoMutateGlobalParameterTbl()

• pgu.delegate$updateTrafoMutateGlobalModelTbl()

• pgu.delegate$updateTrafoMutateGlobalQualityTbl()

• pgu.delegate$updateTrafoMutateGlobalTestsTbl()

• pgu.delegate$updateTrafoMutateGlobalDataTbl()

• pgu.delegate$updateTrafoNormFeature()

• pgu.delegate$updateTrafoNormMethod()

• pgu.delegate$updateTrafoNormGui()

• pgu.delegate$trafoNormMutate()

• pgu.delegate$updateTrafoNormFeatureGraphic()

• pgu.delegate$resetTrafoNormGui()

• pgu.delegate$updateTrafoNormFeatureStatisticsTbl()

• pgu.delegate$updateTrafoNormStatisticsTbl()

• pgu.delegate$updateTrafoNormParameterTbl()

• pgu.delegate$updateTrafoNormDataTbl()

• pgu.delegate$imputeMissingsAnalyze()

• pgu.delegate$updateImputeMissingsGraphic()

• pgu.delegate$updateImputeMissingsStatisticsTbl()

• pgu.delegate$updateImputeMissingsDistributionTbl()

• pgu.delegate$updateImputeMissingCharacteristicsGraphic()

• pgu.delegate$updateImputeMissingsCharacteristicsTbl()

• pgu.delegate$updateImputeMissingsDetailTbl()

• pgu.delegate$updateImputeOutliersMethod()

• pgu.delegate$updateImputeOutliersFeature()

• pgu.delegate$updateImputeOutliersAlpha()

• pgu.delegate$updateImputeOutliersEpsilon()

• pgu.delegate$updateImputeOutliersMinSamples()

• pgu.delegate$updateImputeOutliersGamma()

• pgu.delegate$updateImputeOutliersNu()

• pgu.delegate$updateImputeOutliersCutoff()

• pgu.delegate$updateImputeOutliersK()

• pgu.delegate$updateImputeOutliersSeed()

• pgu.delegate$updateImputeOutliersGui()

• pgu.delegate$resetImputeOutliersGui()

• pgu.delegate$imputeOutliersDetect()

• pgu.delegate$updateImputeOutliersGraphic()

• pgu.delegate$updateImputeOutliersFeatureGraphic()

• pgu.delegate$updateImputeOutliersFeatureTbl()

• pgu.delegate$updateImputeOutliersStatisticsTbl()

• pgu.delegate$updateImputeOutliersDetailTbl()

• pgu.delegate$updateImputeMutateFeature()

• pgu.delegate$updateImputeMutateMethod()

• pgu.delegate$updateImputeMutateNNeighbors()

• pgu.delegate$updateImputeMutatePredFrac()

• pgu.delegate$updateImputeMutateOutfluxThr()

• pgu.delegate$updateImputeMutateSeed()

• pgu.delegate$updateImputeMutateIterations()

• pgu.delegate$updateImputeMutateGui()

• pgu.delegate$resetImputeMutateGui()

• pgu.delegate$imputeMutateMutate()

• pgu.delegate$updateImputeFluxGraphic()

• pgu.delegate$updateImputeMutateGraphic()

• pgu.delegate$updateImputeMutateStatisticsTbl()

• pgu.delegate$updateImputeMutateDistributionTbl()

• pgu.delegate$updateImputeMutateFeatureDetailGraphic()

• pgu.delegate$updateImputeMutateFeatureDetailTbl()

• pgu.delegate$updateImputeMutateDetailTbl()

• pgu.delegate$updateImputeMutateDataTbl()

• pgu.delegate$validate()

• pgu.delegate$updateAnalysisValidationGui()

• pgu.delegate$updateAnalysisValidationGraphic()

• pgu.delegate$updateAnalysisValidationTestTbl()

• pgu.delegate$updateCentralMomentsOrgTbl()

• pgu.delegate$updateCentralMomentsImpTbl()

• pgu.delegate$updateCentralMomentsDeltaTbl()

• pgu.delegate$updateCorrelationValidationScatterGraphic()

• pgu.delegate$updateCorrelationValidationBoxPlotGraphic()

• pgu.delegate$updateCorrelationValidationDeviationTbl()

• pgu.delegate$updateCorrelationValidationDataTbl()

• pgu.delegate$exportFileName()

• pgu.delegate$exportData()

• pgu.delegate$reportFileName()

• pgu.delegate$writeReport()

• pgu.delegate$hide_outdated_results()

• pgu.delegate$update_help_html()

• pgu.delegate$clone()

Method new()

Clears the heap and indicates that instance of pgu.delegate is removed from heap.

Creates and returns a new pgu.delegate object.

Usage

pgu.delegate$new(data = "tbl_df")

Arguments

Returns

A new pgu.delegate object. (pguIMP::pgu.delegate)

Method print()

Prints instance variables of a pgu.delegate object.

Usage

pgu.delegate$print()

Returns

string

Method update_import_gui()

Updates the import gui

Usage

pgu.delegate$update_import_gui(input, output, session)

Arguments

Method query_data()

Manages the data upload to the R server. Updates the instance class status.

Usage

pgu.delegate$query_data(input, output, session)

Arguments

Method import_data()

Imports uploaded data from the R server into the instance variable rawData. Updates the instance class status.

Usage

pgu.delegate$import_data(input, output, session)

Arguments

Method update_import_data_Types_tbl()

Updates the tbl.importDataTypes table.

Usage

pgu.delegate$update_import_data_Types_tbl(input, output, session)

Arguments

Method update_import_data_statistics_tbl()

Updates the tbl.importDataStatistics table.

Usage

pgu.delegate$update_import_data_statistics_tbl(input, output, session)

Arguments

Method update_import_missings_statistics_tbl()

Updates the tbl.importMissingsStatistics table.

Usage

pgu.delegate$update_import_missings_statistics_tbl(input, output, session)

Arguments

Method update_filter_select_tbl()

Updates the tbl.filter table.

Usage

pgu.delegate$update_filter_select_tbl(input, output, session)

Arguments

Method update_filter()

Queries the filter parameters selected by the user in the gui and stores them in the instance variable filterSet.

Usage

pgu.delegate$update_filter(input, output, session)

Arguments

Method update_filter_inverse()

Queries the filter parameters selected by the user in the gui inverts them and stores them in the instance variable filterSet.

Usage

pgu.delegate$update_filter_inverse(input, output, session)

Arguments

Method reset_filter()

Generates a filter set that selects the whole data frame. Stores them in the instance variable filterSet. Updates the gui.

Usage

pgu.delegate$reset_filter(input, output, session)

Arguments

Method filter_data()

Filters the data corresponding to the user defined parameters stored in the instance variable filterSet. Results are stored in the instance variables filteredData and filteredMetadata. Updated the instance variable filterSet.

Usage

pgu.delegate$filter_data(input, output, session)

Arguments

Method update_filter_statistics_tbl()

Updates the tbl.filterStatistics table.

Usage

pgu.delegate$update_filter_statistics_tbl(input, output, session)

Arguments

Method update_filter_missings_tbl()

Updates the tbl.filterMissings table.

Usage

pgu.delegate$update_filter_missings_tbl(input, output, session)

Arguments

Method update_exploration_gui()

Updates the gui.

Usage

pgu.delegate$update_exploration_gui(input, output, session)

Arguments

Method update_exploration_abscissa()

Transfers the information oabout the selected abscissa attribute to the explorer class.

Usage

pgu.delegate$update_exploration_abscissa(input, output, session)

Arguments

Method update_exploration_ordinate()

Transfers the information oabout the selected ordinate attribute to the explorer class.

Usage

pgu.delegate$update_exploration_ordinate(input, output, session)

Arguments

Method update_exploration_graphic()

Updates the exploration abscissa vs. ordinate scatter plot corresponding to the respective user defined attributes.

Usage

pgu.delegate$update_exploration_graphic(input, output, session)

Arguments

Method update_exploration_abscissa_graphic()

Updates the abscissa compound plot corresponding to the respective user defined attributes.

Usage

pgu.delegate$update_exploration_abscissa_graphic(input, output, session)

Arguments

Method update_exploration_ordinate_graphic()

Updates the ordinate compound plot corresponding to the respective user defined attributes.

Usage

pgu.delegate$update_exploration_ordinate_graphic(input, output, session)

Arguments

Method update_exploration_abscissa_table()

Updates the numerical abscissa analysis table. corresponding to the respective user defined attributes.

Usage

pgu.delegate$update_exploration_abscissa_table(input, output, session)

Arguments

Method update_exploration_ordinate_table()

Updates the numerical ordinate analysis table. corresponding to the respective user defined attributes.

Usage

pgu.delegate$update_exploration_ordinate_table(input, output, session)

Arguments

Method reset_loq_values()

Initializes the LOQ object after filtering.

Usage

pgu.delegate$reset_loq_values(input, output, session)

Arguments

Method update_loq_upload_gui()

Updates the gui.

Usage

pgu.delegate$update_loq_upload_gui(input, output, session)

Arguments

Method query_loq()

Manages the loq data upload to the R server.

Usage

pgu.delegate$query_loq(input, output, session)

Arguments

Method import_loq()

Imports the loq data upload to the R server.

Usage

pgu.delegate$import_loq(input, output, session)

Arguments

Method update_loq_define_gui()

Updates the gui.

Usage

pgu.delegate$update_loq_define_gui(input, output, session)

Arguments

Method update_loq_define_feature()

Updates the gui.

Usage

pgu.delegate$update_loq_define_feature(input, output, session)

Arguments

Method update_loq_define_lloq()

Updates the gui.

Usage

pgu.delegate$update_loq_define_lloq(input, output, session)

Arguments

Method update_loq_define_uloq()

Updates the gui.

Usage

pgu.delegate$update_loq_define_uloq(input, output, session)

Arguments

Method update_loq_define_table()

Updates the gui.

Usage

pgu.delegate$update_loq_define_table(input, output, session)

Arguments

Method update_loq_define_menu()

Updates the gui.

Usage

pgu.delegate$update_loq_define_menu(input, output, session)

Arguments

Method set_loq_define_values()

Updates loq class.

Usage

pgu.delegate$set_loq_define_values(input, output, session)

Arguments

Method set_loq_define_values_globally()

Updates loq class.

Usage

pgu.delegate$set_loq_define_values_globally(input, output, session)

Arguments

Details

Imports uploaded data from the R server into the instance variable loqData. Updates the instance class status.

Example code:

importLoq = function(input, output, session){
  if (private$.status$query(processName = "dataImported")){
    tryCatch({
      private$.loq$setLoq <- private$.importer$importLoq(self$fileName)
      private$.status$update(processName = "loqImported", value = TRUE)
    },
    error = function(e) {
      private$.status$update(processName = "loqImported", value = FALSE)
      shiny::showNotification(paste(e),type = "error", duration = 10)
    }#error
    )#tryCatch
  }#if
  else{
    private$.status$update(processName = "loqImported", value = FALSE)
    shiny::showNotification(paste("No file uploaded to import. Please upload a valid file first."),type = "error", duration = 10)
  }#else
}, #function

Method update_loq_detect_gui()

Updates the gui.

Usage

pgu.delegate$update_loq_detect_gui(input, output, session)

Arguments

Method update_loq_na_handling()

Updates the si.loqHandling shiny widget corresponding to the respective user defined parameter.

Usage

pgu.delegate$update_loq_na_handling(input, output, session)

Arguments

Method init_detect_loq()

Runs the outlier detection routine of the instance variable outliers. Updates the instance class status.

Usage

pgu.delegate$init_detect_loq(input, output, session)

Arguments

Method detect_loq()

Runs the outlier detection routine of the instance variable outliers. Updates the instance class status.

Usage

pgu.delegate$detect_loq(input, output, session)

Arguments

Method update_loq_detect_statistics_tbl()

Updates the numerical loq statistics analysis table

Usage

pgu.delegate$update_loq_detect_statistics_tbl(input, output, session)

Arguments

Method update_loq_detect_outlier_tbl()

Updates the numerical loq table.

Usage

pgu.delegate$update_loq_detect_outlier_tbl(input, output, session)

Arguments

Method update_loq_detect_statistics_graphic()

Updates the loq statistics graphic.

Usage

pgu.delegate$update_loq_detect_statistics_graphic(input, output, session)

Arguments

Method update_loq_detect_attribute_graphic()

Updates the loq feature compound graphic.

Usage

pgu.delegate$update_loq_detect_attribute_graphic(input, output, session)

Arguments

Method update_loq_detect_attribute_tbl()

Updates the numerical loq feature table.

Usage

pgu.delegate$update_loq_detect_attribute_tbl(input, output, session)

Arguments

Method update_loq_mutate_gui()

Updates the gui.

Usage

pgu.delegate$update_loq_mutate_gui(input, output, session)

Arguments

Method update_lloq_substitute()

Updates the si.lloqSubstitute shiny widget.

Usage

pgu.delegate$update_lloq_substitute(input, output, session)

Arguments

Method update_uloq_substitute()

Updates the si.uloqSubstitute shiny widget.

Usage

pgu.delegate$update_uloq_substitute(input, output, session)

Arguments

Method init_mutate_loq()

Calls the mutation routine of the instance variable loq on the instance variable filteredData. The reult is stored in the instance variable loqMutatedData Updates the instance class status.

Usage

pgu.delegate$init_mutate_loq(input, output, session)

Arguments

Method mutate_loq()

Calls the mutation routine of the instance variable loq on the instance variable filteredData. The reult is stored in the instance variable loqMutatedData Updates the instance class status.

Usage

pgu.delegate$mutate_loq(input, output, session)

Arguments

Method update_loq_mutate_data_tbl()

Updates the numerical loq mutate outliers table.

Usage

pgu.delegate$update_loq_mutate_data_tbl(input, output, session)

Arguments

Method update_loq_mutate_statistics_graphic()

Updates the loq mutate statistics graphic.

Usage

pgu.delegate$update_loq_mutate_statistics_graphic(input, output, session)

Arguments

Method update_loq_mutate_attribute_graphic()

Updates the loq mutate feature graphic.

Usage

pgu.delegate$update_loq_mutate_attribute_graphic(input, output, session)

Arguments

Method init_loq_mutate_attribute_tbl()

Updates the numeric loq mutate feature table.

Usage

pgu.delegate$init_loq_mutate_attribute_tbl(input, output, session)

Arguments

Method update_loq_mutate_attribute_tbl()

Updates the numeric loq mutate feature table.

Usage

pgu.delegate$update_loq_mutate_attribute_tbl(input, output, session)

Arguments

Method optimizeTrafoParameter()

Calls the optimize routine of the instance variable optimizer on the instance variable loqMutatedData. Updates the instance class status.

Usage

pgu.delegate$optimizeTrafoParameter(input, output, session)

Arguments

Method updateDetectedTrafoTypes()

Updates the detected trafo types table.

Usage

pgu.delegate$updateDetectedTrafoTypes(input, output, session)

Arguments

Method updateDetectedTrafoParameter()

Updates the detected trafo parameters table.

Usage

pgu.delegate$updateDetectedTrafoParameter(input, output, session)

Arguments

Method updateTrafoDetectGui()

Updates the gui.

Usage

pgu.delegate$updateTrafoDetectGui(input, output, session)

Arguments

Method updateTrafoMutateFeature()

Updates the si.trafoMutateFeature shiny widget.

Usage

pgu.delegate$updateTrafoMutateFeature(input, output, session)

Arguments

Method updateTrafoMutateType()

Updates the si.trafoMutateType shiny widget.

Usage

pgu.delegate$updateTrafoMutateType(input, output, session)

Arguments

Method updateTrafoMutateLambda()

Updates the ni.trafoMutateLambda shiny widget.

Usage

pgu.delegate$updateTrafoMutateLambda(input, output, session)

Arguments

Method updateTrafoMutateMirror()

Updates the cb.trafoMutateMirror shiny widget.

Usage

pgu.delegate$updateTrafoMutateMirror(input, output, session)

Arguments

Method resetTrafoMutateGui()

Updates the gui.

Usage

pgu.delegate$resetTrafoMutateGui(input, output, session)

Arguments

Method updateTrafoMutateGui()

Updates the gui.

Usage

pgu.delegate$updateTrafoMutateGui(input, output, session)

Arguments

Method trafoMutateFit()

Estimates the optimal transformation parameters. Updates the GUI

Usage

pgu.delegate$trafoMutateFit(input, output, session)

Arguments

Method trafoMutateGlobal()

Calls the transformation routine of the instance variable transformator on the instance variable loqMutatedData. Updates the instance class status.

Usage

pgu.delegate$trafoMutateGlobal(input, output, session)

Arguments

Method trafoMutateFeature()

Calls the transformation routine of the instance variable transformator on a user defined attribute of the instance variable loqMutatedData.

Usage

pgu.delegate$trafoMutateFeature(input, output, session)

Arguments

Method updateTrafoMutateFeatureGraphic()

Updates the trafo mutate feature graphic.

Usage

pgu.delegate$updateTrafoMutateFeatureGraphic(input, output, session)

Arguments

Method updateTrafoMutateFeatureParameterTbl()

Updates the trafo mutate feature patameter table.

Usage

pgu.delegate$updateTrafoMutateFeatureParameterTbl(input, output, session)

Arguments

Method updateTrafoMutateFeatureQualityTbl()

Updates the trafo mutate feature quality table.

Usage

pgu.delegate$updateTrafoMutateFeatureQualityTbl(input, output, session)

Arguments

Method updateTrafoMutateGlobalParameterTbl()

Updates the trafo mutate global parameter table.

Usage

pgu.delegate$updateTrafoMutateGlobalParameterTbl(input, output, session)

Arguments

Method updateTrafoMutateGlobalModelTbl()

Updates the tbl.trafoMutateGlobalModel table.

Usage

pgu.delegate$updateTrafoMutateGlobalModelTbl(input, output, session)

Arguments

Method updateTrafoMutateGlobalQualityTbl()

Updates the tbl.trafoMutateGlobalQuality table.

Usage

pgu.delegate$updateTrafoMutateGlobalQualityTbl(input, output, session)

Arguments

Method updateTrafoMutateGlobalTestsTbl()

Updates the tbl.trafoMutateGlobalTests table.

Usage

pgu.delegate$updateTrafoMutateGlobalTestsTbl(input, output, session)

Arguments

Method updateTrafoMutateGlobalDataTbl()

Updates the tbl.trafoMutateGlobalData table.

Usage

pgu.delegate$updateTrafoMutateGlobalDataTbl(input, output, session)

Arguments

Method updateTrafoNormFeature()

Updates the si.trafoNormFeature shiny widget.

Usage

pgu.delegate$updateTrafoNormFeature(input, output, session)

Arguments

Method updateTrafoNormMethod()

Updates the si.trafoNormMethod shiny widget.

Usage

pgu.delegate$updateTrafoNormMethod(input, output, session)

Arguments

Method updateTrafoNormGui()

Updates the gui.

Usage

pgu.delegate$updateTrafoNormGui(input, output, session)

Arguments

Method trafoNormMutate()

Calls the scale routine of the instance variable normalizer on the instance variable transformedData. Updates the instance class status.

Usage

pgu.delegate$trafoNormMutate(input, output, session)

Arguments

Method updateTrafoNormFeatureGraphic()

Updates the impute norm feature compound graphic.

Usage

pgu.delegate$updateTrafoNormFeatureGraphic(input, output, session)

Arguments

Method resetTrafoNormGui()

Updates the gui.

Usage

pgu.delegate$resetTrafoNormGui(input, output, session)

Arguments

Method updateTrafoNormFeatureStatisticsTbl()

Updates the numerical impute norm analysis table for a user defined feature. corresponding to the respective user defined attributes.

Usage

pgu.delegate$updateTrafoNormFeatureStatisticsTbl(input, output, session)

Arguments

Method updateTrafoNormStatisticsTbl()

Updates the numerical impute norm analysis table. corresponding to the respective user defined attributes.

Usage

pgu.delegate$updateTrafoNormStatisticsTbl(input, output, session)

Arguments

Method updateTrafoNormParameterTbl()

Updates the impute norm parameter table. corresponding to the respective user defined attributes.

Usage

pgu.delegate$updateTrafoNormParameterTbl(input, output, session)

Arguments

Method updateTrafoNormDataTbl()

Updates the impute norm scaled data table. corresponding to the respective user defined attributes.

Usage

pgu.delegate$updateTrafoNormDataTbl(input, output, session)

Arguments

Method imputeMissingsAnalyze()

Calls the missing detection routine of the instance variable imputer on the instance variable normalizedData. Updates the instance class status.

Usage

pgu.delegate$imputeMissingsAnalyze(input, output, session)

Arguments

Method updateImputeMissingsGraphic()

Updates the plt.imputeMissingsSummary graphic.

Usage

pgu.delegate$updateImputeMissingsGraphic(input, output, session)

Arguments

Method updateImputeMissingsStatisticsTbl()

Updates the tbl.imputeMissingsStatistics table.

Usage

pgu.delegate$updateImputeMissingsStatisticsTbl(input, output, session)

Arguments

Method updateImputeMissingsDistributionTbl()

Updates the tbl.imputeMissingsDistribution table.

Usage

pgu.delegate$updateImputeMissingsDistributionTbl(input, output, session)

Arguments

Method updateImputeMissingCharacteristicsGraphic()

Updates the plt.imputeMissingsPairs graphic.

Usage

pgu.delegate$updateImputeMissingCharacteristicsGraphic(input, output, session)

Arguments

Method updateImputeMissingsCharacteristicsTbl()

Updates the tbl.imputeMissingsCharacteristics table.

Usage

pgu.delegate$updateImputeMissingsCharacteristicsTbl(input, output, session)

Arguments

Method updateImputeMissingsDetailTbl()

Updates the tbl.imputeDetectDetail table.

Usage

pgu.delegate$updateImputeMissingsDetailTbl(input, output, session)

Arguments

Method updateImputeOutliersMethod()

Updates the si.imputeOutliersMethod shiny widget.

Usage

pgu.delegate$updateImputeOutliersMethod(input, output, session)

Arguments

Method updateImputeOutliersFeature()

Updates the si.imputeOutliersFeature shiny widget.

Usage

pgu.delegate$updateImputeOutliersFeature(input, output, session)

Arguments

Method updateImputeOutliersAlpha()

Updates the ni.imputeOutliersAlpha shiny widget.

Usage

pgu.delegate$updateImputeOutliersAlpha(input, output, session)

Arguments

Method updateImputeOutliersEpsilon()

Updates the ni.imputeOutliersEpsilon shiny widget.

Usage

pgu.delegate$updateImputeOutliersEpsilon(input, output, session)

Arguments

Method updateImputeOutliersMinSamples()

Updates the ni.imputeOutliersMinSamples shiny widget.

Usage

pgu.delegate$updateImputeOutliersMinSamples(input, output, session)

Arguments

Method updateImputeOutliersGamma()

Updates the ni.imputeOutliersGamma shiny widget.

Usage

pgu.delegate$updateImputeOutliersGamma(input, output, session)

Arguments

Method updateImputeOutliersNu()

Updates the ni.imputeOutliersNu shiny widget.

Usage

pgu.delegate$updateImputeOutliersNu(input, output, session)

Arguments

Method updateImputeOutliersCutoff()

Updates the ni.imputeOutliersCutoff shiny widget.

Usage

pgu.delegate$updateImputeOutliersCutoff(input, output, session)

Arguments

Method updateImputeOutliersK()

Updates the ni.imputeOutliersK shiny widget.

Usage

pgu.delegate$updateImputeOutliersK(input, output, session)

Arguments

Method updateImputeOutliersSeed()

Updates the ni.imputeOutliersSeed shiny widget.

Usage

pgu.delegate$updateImputeOutliersSeed(input, output, session)

Arguments

Method updateImputeOutliersGui()

Updates the gui.

Usage

pgu.delegate$updateImputeOutliersGui(input, output, session)

Arguments

Method resetImputeOutliersGui()

Updates the gui.

Usage

pgu.delegate$resetImputeOutliersGui(input, output, session)

Arguments

Method imputeOutliersDetect()

Calls the detectOutliers routine of the instance variable outliers on the instance variable normalizedData. Updates the instance class status.

Usage

pgu.delegate$imputeOutliersDetect(input, output, session)

Arguments

Method updateImputeOutliersGraphic()

Updates the plt.outliersImputeSummary graphic.

Usage

pgu.delegate$updateImputeOutliersGraphic(input, output, session)

Arguments

Method updateImputeOutliersFeatureGraphic()

Updates the plt.outliersImputeFeature graphic.

Usage

pgu.delegate$updateImputeOutliersFeatureGraphic(input, output, session)

Arguments

Method updateImputeOutliersFeatureTbl()

Updates the numeric outlier feature table.

Usage

pgu.delegate$updateImputeOutliersFeatureTbl(input, output, session)

Arguments

Method updateImputeOutliersStatisticsTbl()

Updates the numerical loq statistics analysis table

Usage

pgu.delegate$updateImputeOutliersStatisticsTbl(input, output, session)

Arguments

Method updateImputeOutliersDetailTbl()

Updates the numerical outlier table.

Usage

pgu.delegate$updateImputeOutliersDetailTbl(input, output, session)

Arguments

Method updateImputeMutateFeature()

Updates the si.imputeMutateFeature shiny widget.

Usage

pgu.delegate$updateImputeMutateFeature(input, output, session)

Arguments

Method updateImputeMutateMethod()

Updates the si.imputeMutateMethod shiny widget.

Usage

pgu.delegate$updateImputeMutateMethod(input, output, session)

Arguments

Method updateImputeMutateNNeighbors()

Updates the ni.imputeMutateNumberOfNeighbors shiny widget.

Usage

pgu.delegate$updateImputeMutateNNeighbors(input, output, session)

Arguments

Method updateImputeMutatePredFrac()

Updates the ni.imputeMutatePredFrac shiny widget.

Usage

pgu.delegate$updateImputeMutatePredFrac(input, output, session)

Arguments

Method updateImputeMutateOutfluxThr()

Updates the ni.imputeMutateOutfluxThr shiny widget.

Usage

pgu.delegate$updateImputeMutateOutfluxThr(input, output, session)

Arguments

Method updateImputeMutateSeed()

Updates the ni.imputeMutateSeed shiny widget.

Usage

pgu.delegate$updateImputeMutateSeed(input, output, session)

Arguments

Method updateImputeMutateIterations()

Updates the ni.imputeMutateIterations shiny widget.

Usage

pgu.delegate$updateImputeMutateIterations(input, output, session)

Arguments

Method updateImputeMutateGui()

Updates the gui.

Usage

pgu.delegate$updateImputeMutateGui(input, output, session)

Arguments

Method resetImputeMutateGui()

Resets the gui.

Usage

pgu.delegate$resetImputeMutateGui(input, output, session)

Arguments

Method imputeMutateMutate()

Calls the mutate imputation site routine of the instance variable imputer on the instance variable transformedData. Updates the instance class status.

Usage

pgu.delegate$imputeMutateMutate(input, output, session)

Arguments

Method updateImputeFluxGraphic()

Updates the plt.imputeMutateFlux graphic.

Usage

pgu.delegate$updateImputeFluxGraphic(input, output, session)

Arguments

Method updateImputeMutateGraphic()

Updates the plt.imputeMutateSummary graphic.

Usage

pgu.delegate$updateImputeMutateGraphic(input, output, session)

Arguments

Method updateImputeMutateStatisticsTbl()

Updates the tbl.imputeMutateStatistics table.

Usage

pgu.delegate$updateImputeMutateStatisticsTbl(input, output, session)

Arguments

Method updateImputeMutateDistributionTbl()

Updates the tbl.imputeMutateDistribution table.

Usage

pgu.delegate$updateImputeMutateDistributionTbl(input, output, session)

Arguments

Method updateImputeMutateFeatureDetailGraphic()

Updates the plt.imputeMutateFeatureDetail graphic.

Usage

pgu.delegate$updateImputeMutateFeatureDetailGraphic(input, output, session)

Arguments

Method updateImputeMutateFeatureDetailTbl()

Updates the tbl.imputeMutateFeatureDetail table.

Usage

pgu.delegate$updateImputeMutateFeatureDetailTbl(input, output, session)

Arguments

Method updateImputeMutateDetailTbl()

Updates the tbl.imputeMutateDetail table.

Usage

pgu.delegate$updateImputeMutateDetailTbl(input, output, session)

Arguments

Method updateImputeMutateDataTbl()

Updates the tbl.imputeMutateData table.

Usage

pgu.delegate$updateImputeMutateDataTbl(input, output, session)

Arguments

Method validate()

Calls the validate routine of the instance variable validator on the instance variables rawData and clenaedData. Updates the instance class status.

Usage

pgu.delegate$validate(input, output, session)

Arguments

Method updateAnalysisValidationGui()

Updates the si.analysisValidationFeature shiny widget.

Usage

pgu.delegate$updateAnalysisValidationGui(input, output, session)

Arguments

Method updateAnalysisValidationGraphic()

Updates the plt.analysisValidationFeature shiny widget.

Usage

pgu.delegate$updateAnalysisValidationGraphic(input, output, session)

Arguments

Method updateAnalysisValidationTestTbl()

Updtates the tbl.analysisValidationTest table.

Usage

pgu.delegate$updateAnalysisValidationTestTbl(input, output, session)

Arguments

Method updateCentralMomentsOrgTbl()

Updtates the tbl.centralMomentsOrg table.

Usage

pgu.delegate$updateCentralMomentsOrgTbl(input, output, session)

Arguments

Method updateCentralMomentsImpTbl()

Updtates the tbl.centralMomentsImp table.

Usage

pgu.delegate$updateCentralMomentsImpTbl(input, output, session)

Arguments

Method updateCentralMomentsDeltaTbl()

Updtates the tbl.centralMomentsDelta table.

Usage

pgu.delegate$updateCentralMomentsDeltaTbl(input, output, session)

Arguments

Method updateCorrelationValidationScatterGraphic()

Updtates the plt.correlationValidationScatter graphic.

Usage

pgu.delegate$updateCorrelationValidationScatterGraphic(input, output, session)

Arguments

Method updateCorrelationValidationBoxPlotGraphic()

Updtates the plt.correlationValidationBoxPlot graphic.

Usage

pgu.delegate$updateCorrelationValidationBoxPlotGraphic(input, output, session)

Arguments

Method updateCorrelationValidationDeviationTbl()

Updtates the tbl.correlationValidationDeviation table.

Usage

pgu.delegate$updateCorrelationValidationDeviationTbl(input, output, session)

Arguments

Method updateCorrelationValidationDataTbl()

Updtates the tbl.correlationValidationData table.

Usage

pgu.delegate$updateCorrelationValidationDataTbl(input, output, session)

Arguments

Method exportFileName()

Creates and returns an export filename.

Usage

pgu.delegate$exportFileName(input, output, session)

Arguments

Returns

export filename (character)

Method exportData()

Exports the pguIMP analysis results

Usage

pgu.delegate$exportData(input, file)

Arguments

Method reportFileName()

Creates and returns a report filename.

Usage

pgu.delegate$reportFileName(input, output, session)

Arguments

Returns

export filename (character)

Method writeReport()

Exports a report on the pguIMP analysis in pdf format.

Usage

pgu.delegate$writeReport(input, file)

Arguments

Method hide_outdated_results()

Updates the gui if analysis parameters change.

Usage

pgu.delegate$hide_outdated_results(input, output, session)

Arguments

Method update_help_html()

Updates the gui if analysis parameters change.

Usage

pgu.delegate$update_help_html(input, output, session)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.delegate$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.explorer

Description

Visual exploration of the pguIMP dataset.

Format

[R6::R6Class] object.

Details

Pariwise anlysis of attributes from the pguIMP dataset. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.explorer$new()

• pgu.explorer$print()

• pgu.explorer$reset()

• pgu.explorer$fit()

• pgu.explorer$scatterPlot()

• pgu.explorer$abscissaBarPlot()

• pgu.explorer$abscissaBoxPlot()

• pgu.explorer$abscissaPlot()

• pgu.explorer$ordinateBarPlot()

• pgu.explorer$ordinateBoxPlot()

• pgu.explorer$ordinatePlot()

• pgu.explorer$clone()

Method new()

Tests if the abscissa attribute is of type numeric.

Tests if the ordinate attribute is of type numeric.

Summarizes the numeric values of a vector.

Calculates the statistics of the abscissa values. Stores the result in the instance variable abscissaStatistics.

Calculates the statistics of the ordinate values. Stores the result in the instance variable ordinateStatistics.

Clears the heap and indicates that instance of 'pgu.explorer' is removed from heap.

Creates and returns a new 'pgu.explorer' object.

Usage

pgu.explorer$new(data_df = "tbl_df")

Arguments

Returns

A new 'pgu.explorer' object. (pguIMP::pgu.optimizer)

Method print()

Prints instance variables of a 'pgu.explorer' object.

Usage

pgu.explorer$print()

Returns

string

Method reset()

Resets the instance of the pgu.explorer class

Usage

pgu.explorer$reset(data_df = "tbl_df", abs = "character", ord = "character")

Arguments

Method fit()

Calculates the abscissa and ordinate statistics

Usage

pgu.explorer$fit()

Method scatterPlot()

Creates and returns a scatter plot abscissa and ordinate value pairs.

Usage

pgu.explorer$scatterPlot()

Returns

Scatter plot (ggplot2::ggplot)

Method abscissaBarPlot()

Creates and returns a histogram from the abscissa values.

Usage

pgu.explorer$abscissaBarPlot()

Returns

Bar plot (ggplot2::ggplot)

Method abscissaBoxPlot()

Creates and returns a box plot from the abscissa values.

Usage

pgu.explorer$abscissaBoxPlot()

Returns

Box plot (ggplot2::ggplot)

Method abscissaPlot()

Creates and returns a compund graphical analysis of the abscissa values.

Usage

pgu.explorer$abscissaPlot()

Returns

Compound plot (gridExtra::grid.arrange)

Method ordinateBarPlot()

Creates and returns a histogram from the ordinate values.

Usage

pgu.explorer$ordinateBarPlot()

Returns

Bar plot (ggplot2::ggplot)

Method ordinateBoxPlot()

Creates and returns a box plot from the ordinate values.

Usage

pgu.explorer$ordinateBoxPlot()

Returns

Box plot (ggplot2::ggplot)

Method ordinatePlot()

Creates and returns a compund graphical analysis of the ordinate values.

Usage

pgu.explorer$ordinatePlot()

Returns

Compound plot (gridExtra::grid.arrange)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.explorer$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.exporter

Description

A class that writes the results of the pguIMP analysis to an Excel file.

Format

[R6::R6Class] object.

Details

Creates a download file name and saves a list of tibbles to an Excel file. Each tibble is written to a separate sheet. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.exporter$new()

• pgu.exporter$finalize()

• pgu.exporter$print()

• pgu.exporter$extractSuffix()

• pgu.exporter$writeDataToExcel()

• pgu.exporter$clone()

Method new()

Creates and returns a new 'pgu.exporter' object.

Usage

pgu.exporter$new()

Returns

A new 'pgu.exporter' object. (pguIMP::pgu.exporter)

Method finalize()

Clears the heap and indicates if instance of 'pgu.exporter' is removed from heap.

Usage

pgu.exporter$finalize()

Method print()

Prints instance variables of a 'pgu.exporter' object.

Usage

pgu.exporter$print()

Returns

string

Method extractSuffix()

extracts the suffix from the fileName

Usage

pgu.exporter$extractSuffix()

Method writeDataToExcel()

writes tibble to an excel file of the name fileName.

Usage

pgu.exporter$writeDataToExcel(obj = "list")

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.exporter$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.fie

Description

Handles file names for the pguIMP shiny web interface.

Format

[R6::R6Class] object.

Details

The class stores filenames and upload specifications for the pguIMP shiny web interface in its instance variables. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.file$new()

• pgu.file$print()

• pgu.file$reset()

• pgu.file$fit()

• pgu.file$predict()

• pgu.file$fit_predict()

• pgu.file$clone()

Method new()

Clears the heap and indicates that instance of 'pgu.file' is removed from heap.

Splits fileName and writes the results in the class' instance variables folderName, baseName, suffix.

Stores the current system time into the instance variable timeString.

Creates and returns a new object of type pgu.file.

Usage

pgu.file$new(
  uploadFileName = "",
  fileName = "",
  sheetIndex = 1,
  separator = ",",
  skipRows = 0,
  columnNames = TRUE,
  naChar = "NA"
)

Arguments

Returns

A new pgu.file object. (pguIMP::pgu.file)

Method print()

Prints the instance variables of the object.

Usage

pgu.file$print()

Returns

string

Method reset()

Resets the instance variables of the object.

Usage

pgu.file$reset(
  uploadFileName = "",
  fileName = "",
  sheetIndex = 1,
  separator = ",",
  skipRows = 0,
  columnNames = TRUE,
  naChar = "NA"
)

Arguments

Method fit()

Extracts information about upload specifications from the instance variables.

Usage

pgu.file$fit()

Method predict()

Predicts an export file name.

Usage

pgu.file$predict(affix = "analysis")

Arguments

Returns

A file name. (string)

Method fit_predict()

Extracts information about upload specifications from the instance variables and predicts an export file name.

Usage

pgu.file$fit_predict(affix = "analysis")

Arguments

Returns

A file name. (string)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.file$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.filter

Description

Filter the pguIMP dataset.

Format

[R6::R6Class] object.

Details

The filtering is done by column and row indices. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.filter$new()

• pgu.filter$print()

• pgu.filter$reset()

• pgu.filter$predict()

• pgu.filter$clone()

Method new()

Resets the filter parameter colIdx to the full dataframe.

Resets the filter parameter rowIdx to the full dataframe.

Clears the heap and indicates that instance of pguIMP::pgu.filter is removed from heap.

Creates and returns a new pguIMP::pgu.filter object.

Usage

pgu.filter$new(data_df = "tbl_df")

Arguments

Returns

A new pguIMP::pgu.filter object. (pguIMP::pgu.filter)

Method print()

Prints instance variables of a pguIMP::pgu.filter object.

Usage

pgu.filter$print()

Returns

string

Method reset()

Resets the filter parameter colIdx and rowIdx to the full dataframe.

Usage

pgu.filter$reset(data_df = "tbl_df")

Arguments

Method predict()

Filters and returns the given data frame.

Usage

pgu.filter$predict(data_df = "tbl_df")

Arguments

Returns

The filtered data frame (tibble::tibble)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.filter$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.importer

Description

Handles the data import

Format

[R6::R6Class] object.

Details

Menages the import of the pguIMP dataset This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.importer$new()

• pgu.importer$finalize()

• pgu.importer$print()

• pgu.importer$suffixIsKnown()

• pgu.importer$importData()

• pgu.importer$importLoq()

• pgu.importer$importMetadata()

• pgu.importer$clone()

Method new()

Creates and returns a new 'pgu.importer' object.

Usage

pgu.importer$new()

Returns

A new 'pgu.importer' object. (pguIMP::pgu.importer)

Method finalize()

Clears the heap and indicates that instance of 'pgu.importer' is removed from heap.

Usage

pgu.importer$finalize()

Method print()

Prints instance variables of a 'pgu.importer' object.

Usage

pgu.importer$print()

Returns

string

Method suffixIsKnown()

Takes an instance of pgu.file and tests if the suffix is valid.

Usage

pgu.importer$suffixIsKnown(obj = "pgu.file")

Arguments

Returns

test result (logical)

Method importData()

Takes an instance of pgu.file imports a dataset.

Usage

pgu.importer$importData(obj = "pgu.file")

Arguments

Returns

data frame (tibble::tibble)

Method importLoq()

Takes an instance of pgu.file imports a loq dataset.

Usage

pgu.importer$importLoq(obj = "pgu.file")

Arguments

Returns

data frame (tibble::tibble)

Method importMetadata()

Takes an instance of pgu.file imports a metadata dataset.

Usage

pgu.importer$importMetadata(obj = "pgu.file")

Arguments

Returns

data frame (tibble::tibble)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.importer$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.imputation

Description

Analyses and substitutes imputation sites in a data set.

Format

[R6::R6Class] object.

Details

Analyses imputation sites in a data set. Replaces imputation sites by missing values and substitutes NAs by classical and ML-powered substitution algorithms. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.imputation$new()

• pgu.imputation$finalize()

• pgu.imputation$print()

• pgu.imputation$gatherImputationSites()

• pgu.imputation$gatherImputationSiteStatistics()

• pgu.imputation$gatherImputationSiteDistribution()

• pgu.imputation$insertImputationSites()

• pgu.imputation$one_hot()

• pgu.imputation$analyzeImputationSites()

• pgu.imputation$imputationSiteIdxByFeature()

• pgu.imputation$nanFeatureList()

• pgu.imputation$average_number_of_predictors()

• pgu.imputation$detectPredictors()

• pgu.imputation$handleImputationSites()

• pgu.imputation$imputeByMedian()

• pgu.imputation$imputeByMean()

• pgu.imputation$imputeByExpectationValue()

• pgu.imputation$imputeByMC()

• pgu.imputation$imputeByKnn()

• pgu.imputation$imputeByMice()

• pgu.imputation$imputeByM5P()

• pgu.imputation$imputationSiteHeatMap()

• pgu.imputation$featureBarPlot()

• pgu.imputation$featureBoxPlotWithSubset()

• pgu.imputation$featurePlot()

• pgu.imputation$fluxPlot()

• pgu.imputation$clone()

Method new()

Creates and returns a new 'pgu.imputation' object.

Usage

pgu.imputation$new(
  seed = 42,
  iterations = 4,
  imputationAgent = "none",
  nNeighbors = 3,
  pred_frac = 1,
  outflux_thr = 0.5
)

Arguments

Returns

A new 'pgu.imputation' object. (pguIMP::pgu.imputation)

Method finalize()

Clears the heap and indicates that instance of 'pgu.imputation' is removed from heap.

Usage

pgu.imputation$finalize()

Method print()

Prints instance variables of a 'pgu.imputation' object.

Usage

pgu.imputation$print()

Returns

string

Method gatherImputationSites()

Gathers imputation sites from pguIMP's missings and outliers class.

Usage

pgu.imputation$gatherImputationSites(
  missings_df = "tbl_df",
  outliers_df = "tbl_df"
)

Arguments

Method gatherImputationSiteStatistics()

Gathers statistical information about imputation sites The information is stored within the classes instance variable 'imputationStatistics'

Usage

pgu.imputation$gatherImputationSiteStatistics(data_df = "tbl_df")

Arguments

Method gatherImputationSiteDistribution()

Gathers the distribution of imputation sites within the data frame. The information is stored within the classes instance variable imputationSiteDistribution.

Usage

pgu.imputation$gatherImputationSiteDistribution(data_df = "tbl_df")

Arguments

Returns

A data frame (tibble::tibble)

Method insertImputationSites()

Takes a dataframe, replaces the imputation sites indicated by the instance variable 'imputationsites' by NA, and returns the mutated dataframe.

Usage

pgu.imputation$insertImputationSites(data_df = "tbl_df")

Arguments

Returns

A mutated version of data_df. (tibble::tibble)

Method one_hot()

Gathers statistical information about missing values in one hot format. The result is stored in the instance variable one_hot_df.

Usage

pgu.imputation$one_hot(data_df = "tbl_df")

Arguments

Method analyzeImputationSites()

Takes a dataframe and analyses the imputation sites.

Usage

pgu.imputation$analyzeImputationSites(data_df = "tbl_df")

Arguments

Method imputationSiteIdxByFeature()

Returns the position of an attribute's imputation sites within a data frame.

Usage

pgu.imputation$imputationSiteIdxByFeature(featureName = "character")

Arguments

Returns

The postion of the imputation sites. (numeric)

Method nanFeatureList()

Characterizes each row of the data frame as either 'complete' or indicates which attribute are missing within the row. If multiple attributes' row entries are missing, the row is characterized by 'multiple'.

Usage

pgu.imputation$nanFeatureList(data_df = "tbl_df")

Arguments

Returns

Vector of row characteristics. (character)

Method average_number_of_predictors()

Calculates the average number of predictors for a given dataframe and minpuc and mincor variables using the mice::quickpred routine.

Usage

pgu.imputation$average_number_of_predictors(
  data_df = "tbl_df",
  minpuc = 0,
  mincor = 0.1
)

Arguments

Returns

Average_number_of_predictors. (numeric)

Method detectPredictors()

Identifies possible predictors for each feature. Analysis results are written to the instance variable pred_mat. Intermediate analysis results are an influx/outflux dataframe that is written to the instance variable flux_df and detect predictors and a list of features that is excluded from the search for possible predictors that is written to the instance variable exclude_vec.

Usage

pgu.imputation$detectPredictors(data_df = "tbl_df")

Arguments

Method handleImputationSites()

Chooses a cleaning method based upon the instance variable 'imputationAgent' and handles the imputation sites in the dataframe. Returns a cleaned data set. Display the progress if shiny is loaded.

Usage

pgu.imputation$handleImputationSites(data_df = "tbl_df", progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByMedian()

Substitutes imputation sites by the median of the respective attribute. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByMedian(data_df = "tbl_df", progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByMean()

Substitutes imputation sites by the aritmertic mean of the respective attribute. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByMean(data_df = "tbl_df", progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByExpectationValue()

Substitutes imputation sites by the expectation value of the respective attribute. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByExpectationValue(
  data_df = "tbl_df",
  progress = "Progress"
)

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByMC()

Substitutes imputation sites by values generated by a monte carlo simulation. The procedure runs several times as defined by the instance variable 'iterations'. The run with the best result is identified and used for substitution. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByMC(data_df = "tbl_df", progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByKnn()

Substitutes imputation sites by predictions of a KNN analysis of the whole dataframe. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByKnn(data_df = "tbl_df", progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByMice()

Substitutes imputation sites by values generated by a different methods of the mice package. The procedure runs several times as defined by the instance variable 'iterations'. The run with the best result is identified and used for substitution. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByMice(data_df, progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputeByM5P()

Substitutes imputation sites by predictions of a M5P tree trained on the whole dataframe. Returns the cleaned dataframe. Display the progress if shiny is loaded.

Usage

pgu.imputation$imputeByM5P(data_df = "tbl_df", progress = "Progress")

Arguments

Returns

Cleaned dataframe. (tibble:tibble)

Method imputationSiteHeatMap()

Displays the distribution of missing values in form of a heatmap.

Usage

pgu.imputation$imputationSiteHeatMap()

Returns

A heatmap plot. (ggplot2::ggplot)

Method featureBarPlot()

Displays the distribution of an attribute values as histogram.

Usage

pgu.imputation$featureBarPlot(data_df = "tbl_df", feature = "character")

Arguments

Returns

A histogram. (ggplot2::ggplot)

Method featureBoxPlotWithSubset()

Displays the distribution of an attribute's values as box plot.

Usage

pgu.imputation$featureBoxPlotWithSubset(
  data_df = "tbl_df",
  feature = "character"
)

Arguments

Returns

A box plot. (ggplot2::ggplot)

Method featurePlot()

Displays the distribution of an attribute's values as a composition of a box plot and a histogram.

Usage

pgu.imputation$featurePlot(data_df = "tbl_df", feature = "character")

Arguments

Returns

A composite plot. (ggplot2::ggplot)

Method fluxPlot()

Displays an influx/outflux plot

Usage

pgu.imputation$fluxPlot()

Returns

A composite plot. (ggplot2::ggplot)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.imputation$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.limitsOfQuantification

Description

Handles values in the pguIMP dataset that exceed the limits of quantification. This object is used by the shiny based gui and is not for use in individual R-scripts!

Format

R6::R6Class object.

Details

more information

Active bindings

Methods

The following public methods are available:

- 'new()': Creates a new pgu.limitsOfQuantification object - 'print()': Prints instance variables - 'reset()': Resets the object - 'fit()': Analyzes data for LOQ violations - 'predict()': Applies LOQ corrections - 'attribute_lloq()', 'attribute_uloq()': Get LOQ values - 'set_attribute_lloq()', 'set_attribute_uloq()': Set LOQ values - 'attribute_outliers()': Get outliers for an attribute - 'plot_loq_distribution()': Plot LOQ statistics - 'attribute_bar_plot()', 'attribute_box_plot_with_subset()', 'attribute_plot()': Create diagnostic plots - 'clone()': Clone the object

Author(s)

Sebastian Malkusch

See Also

Other pguIMP classes: pgu.missings

pgu.missings

Description

Detects and substitutes missing values from data set.

Format

R6::R6Class object.

Details

Detects missing values in the transformed and normalized data set. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

The following public methods are available:

- 'new()': Creates a new pgu.missings object - 'print()': Prints instance variables - 'resetImputationParameter()': Resets imputation parameters - 'featureIdx()': Get feature position - 'filterFeatures()': Filter features with missings - 'gatherMeasurements()', 'gatherMissings()', 'gatherExistings()', 'gatherFractionOfMissings()': Gather missing value statistics - 'gatherImputationStatistics()': Collect imputation statistics - 'one_hot()': Create one-hot encoding of missings - 'detectImputationSites()': Detect missing value positions - 'imputationSiteDistribution()': Get missing value distribution - 'imputationSiteHeatMap()': Create missing value heatmap - 'clone()': Clone the object

Author(s)

Sebastian Malkusch

See Also

Other pguIMP classes: pgu.limitsOfQuantification

pgu.missingsCharacterizer

Description

A class that characterizes the origin of missing values.

Format

[R6::R6Class] object.

Details

A class that characterizes the origin of missing values. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.missingsCharacterizer$new()

• pgu.missingsCharacterizer$finalize()

• pgu.missingsCharacterizer$print()

• pgu.missingsCharacterizer$reset()

• pgu.missingsCharacterizer$analyze()

• pgu.missingsCharacterizer$plot_pair_dist()

• pgu.missingsCharacterizer$clone()

Method new()

Creates and returns a new 'pgu.missingsCharacterizer' object.

Usage

pgu.missingsCharacterizer$new(data_df = "tbl_df")

Arguments

Returns

A new 'pgu.missingsCharacterizer' object. (pguIMP::pgu.missingsCharacterizer)

Method finalize()

Clears the heap and indicates if instance of 'pgu.missingsCharacterizer' is removed from heap.

Usage

pgu.missingsCharacterizer$finalize()

Method print()

Prints instance variables of a 'pgu.missingsCharacterizer' object.

Usage

pgu.missingsCharacterizer$print()

Returns

string

Method reset()

Takes a dataframe that will be analyzed using the analyze function and resets the instance variables.

Usage

pgu.missingsCharacterizer$reset(data_df = "tbl_df")

Arguments

Method analyze()

resets the instance variables and analyzes a dataframe.

Usage

pgu.missingsCharacterizer$analyze(data_df = "tbl_df", progress = "Progress")

Arguments

Method plot_pair_dist()

Plots the analysis result.

Usage

pgu.missingsCharacterizer$plot_pair_dist(data_df = "tbl_df")

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.missingsCharacterizer$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.model

Description

Comprises a list of models for data manipulation.

Format

[R6::R6Class] object.

Details

Comprises a list of pgu.normDist objects and model parameters. These can be used to scale data. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.model$new()

• pgu.model$finalize()

• pgu.model$print()

• pgu.model$resetModelParameter()

• pgu.model$resetModelList()

• pgu.model$resetModel()

• pgu.model$setNormDist()

• pgu.model$featureIdx()

• pgu.model$fitFeature()

• pgu.model$fitData()

• pgu.model$logFitResultsFeature()

• pgu.model$logFailedFitResultsFeature()

• pgu.model$scaleNumeric()

• pgu.model$scaleData()

• pgu.model$rescaleNumeric()

• pgu.model$rescaleData()

• pgu.model$modelParameterData()

• pgu.model$modelParameterFeature()

• pgu.model$modelQualityData()

• pgu.model$modelQualityFeature()

• pgu.model$fitResultData()

• pgu.model$fitResultFeature()

• pgu.model$testResultData()

• pgu.model$testResultFeature()

• pgu.model$plotModel()

• pgu.model$clone()

Method new()

Creates and returns a new 'pgu.model' object.

Usage

pgu.model$new(data = "tbl_df")

Arguments

Returns

A new 'pgu.model' object. (pguIMP::pgu.model)

Method finalize()

Clears the heap and indicates that instance of 'pgu.model' is removed from heap.

Usage

pgu.model$finalize()

Method print()

Prints instance variables of a 'pgu.model' object.

Usage

pgu.model$print()

Returns

string

Method resetModelParameter()

Resets instance variable 'modelParameter'

Usage

pgu.model$resetModelParameter(data = "tbl_df")

Arguments

Method resetModelList()

Resets instance variable 'modelList'

Usage

pgu.model$resetModelList(data = "tbl_df")

Arguments

Method resetModel()

Resets instance variable 'modelList'. Resets instance variable 'modelParameter'. Displays progress if shiny is loaded.

Usage

pgu.model$resetModel(data = "tbl_df", progress = "Progress")

Arguments

Method setNormDist()

Stores the information of a pgu.norDist object in an entry of the instance variable 'modelList'

Usage

pgu.model$setNormDist(data = "pgu.normDist", feature = "character")

Arguments

Method featureIdx()

Returns the index of a pgu.normDist object wihtin the instance variable 'modelParameter'.

Usage

pgu.model$featureIdx(feature = "character")

Arguments

Returns

Index of attribute entry in dataframe (numeric)

Method fitFeature()

Runs the fit function of a pgu.normDist object at a user denied position within the instance variable modelList.

Usage

pgu.model$fitFeature(feature = "character")

Arguments

Method fitData()

Loops through all attributes and calls the object's ftiFeature function. Displays progress if shiny is loaded.

Usage

pgu.model$fitData(progress = "Progress")

Arguments

Method logFitResultsFeature()

Stores results from fitting procedure of a user defined attribute into the corrsponding attribute of instance variable 'modelParameter'.

Usage

pgu.model$logFitResultsFeature(feature = "character")

Arguments

Method logFailedFitResultsFeature()

Stores results from fitting procedure of a user defined attribute into the corrsponding attribute of instance variable 'modelParameter' in case of a failed fitting routine.

Usage

pgu.model$logFailedFitResultsFeature(feature = "character")

Arguments

Method scaleNumeric()

Scales numeric data based upon the model of a user defined attribute.

Usage

pgu.model$scaleNumeric(value = "numeric", feature = "character")

Arguments

Returns

scaled version of the given vector (numeric)

Method scaleData()

Scales a dataframe based upon a list of models stored in the instance variable modelList..

Usage

pgu.model$scaleData(data = "tbl_df")

Arguments

Returns

scaled version of the given dataframe (tibble::tibble)

Method rescaleNumeric()

Re-scales numeric data based upon the model of a user defined attribute.

Usage

pgu.model$rescaleNumeric(value = "numeric", feature = "character")

Arguments

Returns

Re-scaled version of the given vector (numeric)

Method rescaleData()

Re-scales a dataframe based upon a list of models stored in the instance variable modelList..

Usage

pgu.model$rescaleData(data = "tbl_df")

Arguments

Returns

Re-scaled version of the given dataframe (tibble::tibble)

Method modelParameterData()

Returns the model parameter (expectation value, standard deviation).

Usage

pgu.model$modelParameterData()

Returns

Dataframe comprising model parameter. (tibble::tibble)

Method modelParameterFeature()

Returns the model parameter (expectation value, standard deviation) for a user deined attribute.

Usage

pgu.model$modelParameterFeature(feature = "character")

Arguments

Returns

Dataframe comprising model parameter. (tibble::tibble)

Method modelQualityData()

Returns the model parameters connected to model quality.

Usage

pgu.model$modelQualityData()

Returns

Dataframe comprising model parameter. (tibble::tibble)

Method modelQualityFeature()

Returns the model parameters connected to model quality for a user deined attribute.

Usage

pgu.model$modelQualityFeature(feature = "character")

Arguments

Returns

Dataframe comprising model parameter. (tibble::tibble)

Method fitResultData()

Returns the model fit results.

Usage

pgu.model$fitResultData()

Returns

Dataframe comprising model fit results. (tibble::tibble)

Method fitResultFeature()

Returns the model fit results for a user deined attribute.

Usage

pgu.model$fitResultFeature(feature = "character")

Arguments

Returns

Dataframe comprising model fit results. (tibble::tibble)

Method testResultData()

Returns the hypothesis test results.

Usage

pgu.model$testResultData()

Returns

Dataframe comprising the hypothesis test results. (tibble::tibble)

Method testResultFeature()

Returns the hypothesis test results. for a user deined attribute.

Usage

pgu.model$testResultFeature(feature = "character")

Arguments

Returns

Dataframe comprising the hypothesis test results. (tibble::tibble)

Method plotModel()

Creates and returns a composite graphical analysis of the modeling procedure of a user defined attribute.

Usage

pgu.model$plotModel(feature = "character")

Arguments

Returns

Composite result plot. (ggplot2::ggplot)

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.model$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.normDist

Description

Compares the distribution of a single attribute's values to normal distribution by using several statistic tests.

Format

[R6::R6Class] object.

Details

The distribution of a single value is tested for normality by Shapiro-Wilk test, Kolmogorov-Smirnov test, Anderson-Darling test. The expectation value and standard deviation of a normal distribution representing the data are determined by maximizing the log Likelihood with respect to the expectation value and standard deviation. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods

Public methods

• pgu.normDist$new()

• pgu.normDist$finalize()

• pgu.normDist$print()

• pgu.normDist$resetNormDist()

• pgu.normDist$resetFail()

• pgu.normDist$optimize()

• pgu.normDist$createHistogram()

• pgu.normDist$normalQQData()

• pgu.normDist$test.shapiro()

• pgu.normDist$test.kolmogorow()

• pgu.normDist$test.anderson()

• pgu.normDist$fitResult()

• pgu.normDist$testResult()

• pgu.normDist$testResultCompendium()

• pgu.normDist$plotHistogram()

• pgu.normDist$plotResiduals()

• pgu.normDist$plotResidualDist()

• pgu.normDist$plotRawResidualDist()

• pgu.normDist$plotRawDataDist()

• pgu.normDist$normalQQPlot()

• pgu.normDist$fit()

• pgu.normDist$clone()

Method new()

Creates and returns a new 'pgu.normDist' object.

Usage

pgu.normDist$new(data = "tbl_df")

Arguments

Returns

A new 'pgu.normDist' object. (pguIMP::pgu.normDist)

Method finalize()

Clears the heap and indicates that instance of 'pgu.normDist' is removed from heap.

Usage

pgu.normDist$finalize()

Method print()

Prints instance variables of a 'pgu.normDist' object.

Usage

pgu.normDist$print()

Returns

string

Method resetNormDist()

Resets instance variables

Usage

pgu.normDist$resetNormDist(data = "tbl_df")

Arguments

Method resetFail()

Resets instance variables in case of a failed analysis.

Usage

pgu.normDist$resetFail()

Method optimize()

Optimizes the logLikelihood between the data and a normal distribution with respect to the expectation value and standard deviation. The quality of the best model is calculated subsequently.

Usage

pgu.normDist$optimize()

Method createHistogram()

Creates a histogram from the instance variable 'rawData'. The histogram is stored in the instance variable 'histogram'.

Usage

pgu.normDist$createHistogram()

Method normalQQData()

Performes a qq-analysis of the instance variable 'rawData' The qq-analysis is stored in the attributes 'sample_quantile' and 'theoretical_quantile' of the instance variable 'rawData'.

Usage

pgu.normDist$normalQQData()

Method test.shapiro()

Performes Shapiro-Wilk's test for normality on the instance variable 'rawData'. The test result is stored in the instance variable 'w.shapiro'. The p-value of the test is stored in the instance variable 'p.shapiro'

Usage

pgu.normDist$test.shapiro()

Method test.kolmogorow()

Performes Kolmogorow-Smirnow's test for normality on the instance variable 'rawData'. The test result is stored in the instance variable 'd.kolmogorow'. The p-value of the test is stored in the instance variable 'p.kolmogorow'

Usage

pgu.normDist$test.kolmogorow()

Method test.anderson()

Performes Anderson-Darling's test for normality on the instance variable 'rawData'. The test result is stored in the instance variable 'a.anderson'. The p-value of the test is stored in the instance variable 'p.anderson'

Usage

pgu.normDist$test.anderson()

Method fitResult()

Returns the result of the classes optimize function in form of a formated string.

Usage

pgu.normDist$fitResult()

Returns

String of the results of the fitting routine (character)

Method testResult()

Returns the result of the classes test functions in form of a formated string.

Usage

pgu.normDist$testResult(testName = "Shapiro-Wilk")

Arguments

Returns

String of the results of the testing routine (character)

Method testResultCompendium()

Returns the result of the classes test functions 'Shapiro-Wilk', 'Kolmogorow-Smirnow' and 'Anderson-Darling' in form of a formated string. (character)

Usage

pgu.normDist$testResultCompendium()

Returns

String of the results of the testing routine (character)

Method plotHistogram()

Displays the instance variable 'histogram' in form of a bar plot and overlays the corresponding normal distribution.

Usage

pgu.normDist$plotHistogram()

Returns

A bar plot. (ggplot2::ggplot)

Method plotResiduals()

Displays the residuals between the instance variable 'histogram' and the corresponding normal distribution.

Usage

pgu.normDist$plotResiduals()

Returns

A scatter plot. (ggplot2::ggplot)

Method plotResidualDist()

Displays the distribution of the residuals between the distribution of the instance variable 'histogram' in form of a histogram.

Usage

pgu.normDist$plotResidualDist()

Returns

A bar plot. (ggplot2::ggplot)

Method plotRawResidualDist()

Displays the distribution of the residuals between the distribution of the instance variable 'rawData' in form of a histogram.

Usage

pgu.normDist$plotRawResidualDist()

Returns

A bar plot. (ggplot2::ggplot)

Method plotRawDataDist()

Displays the distribution of the instance variable 'rawData' in form of a histogram.

Usage

pgu.normDist$plotRawDataDist()

Returns

A bar plot. (ggplot2::ggplot)

Method normalQQPlot()

Displays a qqplot of the instance variable 'rawData'.

Usage

pgu.normDist$normalQQPlot()

Returns

A qq-plot. (ggplot2::ggplot)

Method fit()

Runs the optimization process and performs all implemented quality controls. Additionally performs hypothesis tests for normality.

Usage

pgu.normDist$fit()

Method clone()

The objects of this class are cloneable with this method.

Usage

pgu.normDist$clone(deep = FALSE)

Arguments

Author(s)

Sebastian Malkusch

pgu.normalizer

Description

Normalization of data. Part of pguIMP.

Format

[R6::R6Class] object.

Details

Performs a data normalization in order to achieve a standardized version of the dataframe. This object is used by the shiny based gui and is not for use in individual R-scripts!

Active bindings

Methods