pgu.validator: pgu.validator
In pguIMP: 'pguIMP'
Description
Format
Details
Active bindings
Methods
Author(s)
Description
Validates that the distribution is not significantly altered by the imputation process.
This object is used by the shiny based gui and is not for use in individual R-scripts!
Format
R6::R6Class object.
Details
Takes two distributions (before and after imputation).
Performs a Wilcoxon-Mann-Whitney U test.
Performs a Kolmogorow-Smirnow test.
Active bindings
testStatistics_dfReturns the instance variable testStatistics_df.
(tibble::tibble)
centralMoments_orgReturns the instance variable centralMoments_org
(tibble::tibble)
centralMoments_impReturns the instance variable centralMoments_imp
(tibble::tibble)
centralMoments_deltaReturns the instance variable centralMoments_delta
(tibble::tibble)
featuresReturns the instance variable features
(character)
seedReturns the instance variable seed
(integer)
setSeedSets the instance variable seed.
(numeric)
Methods
Public methods
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Method new()
Creates and returns a new pgu.validator object.
Usage
pgu.validator$new(seed = 42)
Arguments
seedSet the instance variable seed.
(integer)
Returns
A new pgu.validator object.
(pguIMP::pgu.validator)
Method finalize()
Clears the heap and
indicates that instance of pgu.validator is removed from heap.
Usage
pgu.validator$finalize()
Method print()
Prints instance variables of a pgu.validator object.
Usage
pgu.validator$print()
Returns
string
Method resetValidator()
Resets instance variables
Usage
pgu.validator$resetValidator()
Method kolmogorowTestFeature()
Performs a comparison between the original and the imputated distribution of a given feature
using a two-sided Kolmorogow-Smirnow test with simulated p-vaue distribution.
Usage
pgu.validator$kolmogorowTestFeature(
org = "numeric",
imp = "numeric",
feature = "character"
)
Arguments
orgOriginal data to be analzed.
(numeric)
impImputed data to be analyzed.
(numeric)
featureFeature name of the analyzed distributions.
(character)
Returns
One row dataframe comprising the test results.
(tibble::tibble)
Method wilcoxonTestFeature()
Performs a comparison between the original and the imputated distribution of a given feature
using a two-sided Wilcoxon/Mann-Whitney test.
Usage
pgu.validator$wilcoxonTestFeature(
org = "numeric",
imp = "numeric",
feature = "character"
)
Arguments
orgOriginal data to be analzed.
(numeric)
impImputed data to be analyzed.
(numeric)
featureFeature name of the analyzed distributions.
(character)
Returns
One row dataframe comprising the test results.
(tibble::tibble)
Method centralMomentsFeature()
Estimates estimates the central moments of the given distribution.
Usage
pgu.validator$centralMomentsFeature(values = "numeric", feature = "character")
Arguments
valuesData to be analzed.
(numeric)
featureFeature name of the analyzed distributions.
(character)
Returns
One row dataframe comprising the statistics.
(tibble::tibble)
Method validate()
Validates the feature distributions of the original and the imputated dataframe“
using a two-sided Kolmorogow-Smirnow test and a two-sided Wilcoxon/Mann-Whitney test.
The result is stored in the instance variables testStatistics_dfand 'distributionStatistics_df'.
Displays the progress if shiny is loaded.
Usage
pgu.validator$validate(
org_df = "tbl_df",
imp_df = "tbl_df",
progress = "Progress"
)
Arguments
org_dfOriginal dataframe to be analzed.
(tibble::tibble)
imp_dfImputed dataframe to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
Method featurePdf()
Receives a dataframe and plots the pareto density of the features 'org_pdf' and 'imp_pdf'.
Returns the plot
Usage
pgu.validator$featurePdf(data_df = "tbl_df")
Arguments
data_dfdataframe to be plotted
(tibble::tibble)
Returns
A ggplot2 object
(ggplot2::ggplot)
Method featureCdf()
Receives a dataframe and plost the feature 'x' against the features 'org_cdf' and 'imp_cdf'.
Returns the plot
Usage
pgu.validator$featureCdf(data_df = "tbl_df")
Arguments
data_dfdataframe to be plotted
(tibble::tibble)
Returns
A ggplot2 object
(ggplot2::ggplot)
Method featureVs()
Receives two numeric vectors 'org' and 'imp'. Plots the qq-plot of both vectors.
Returns the plot
Usage
pgu.validator$featureVs(org = "numeric", imp = "numeric")
Arguments
orgNumric vector comprising the original data.
(numeric)
impNumeric vector comprising the imputed data.
(numeric)
Returns
A ggplot2 object
(ggplot2::ggplot)
Method featureBoxPlot()
Receives a dataframe and information about the lloq and uloq and retuns a boxplot
Usage
pgu.validator$featureBoxPlot(
data_df = "tbl_df",
lloq = "numeric",
uloq = "numeric",
feature = "character"
)
Arguments
data_dfDataframe to be analyzed
(tibble::tibble)
lloqlower limit of quantification
(numeric)
uloqupper limit of quantification
(numeric)
featureFeature name
(character)
Returns
A ggplot2 object
(ggplot2::ggplot)
Method featurePlot()
Receives two numeric dataframes 'org_df' and 'imp_df', and a feature name.
Creates a compund plot of the validation results for the given feature..
Returns the plot
Usage
pgu.validator$featurePlot(
org_df = "tbl_df",
imp_df = "tbl_df",
lloq = "numeric",
uloq = "numeric",
impIdx_df = "tbl_df",
feature = "character"
)
Arguments
org_dfDataframe comprising the original data.
(tibble::tibble)
imp_dfDataframe comprising the imputed data.
(tibble::tibble)
lloqlower limit of quantification
(numeric)
uloqupper limit of quantification
(numeric)
impIdx_dfdataframe comprising information about imputation sites
(tibble::tibble)
featureFeature name.
(character)
Returns
A ggplot2 object
(ggplot2::ggplot)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.validator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
pguIMP documentation built on Sept. 30, 2021, 5:08 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Format Details Active bindings Methods Author(s)
Description
Validates that the distribution is not significantly altered by the imputation process. This object is used by the shiny based gui and is not for use in individual R-scripts!
Format
R6::R6Class object.
Details
Takes two distributions (before and after imputation). Performs a Wilcoxon-Mann-Whitney U test. Performs a Kolmogorow-Smirnow test.
Active bindings
testStatistics_dfReturns the instance variable
testStatistics_df. (tibble::tibble)centralMoments_orgReturns the instance variable
centralMoments_org(tibble::tibble)centralMoments_impReturns the instance variable
centralMoments_imp(tibble::tibble)centralMoments_deltaReturns the instance variable
centralMoments_delta(tibble::tibble)featuresReturns the instance variable
features(character)seedReturns the instance variable seed (integer)
setSeedSets the instance variable seed. (numeric)
Methods
Public methods
Method new()
Creates and returns a new pgu.validator object.
Usage
pgu.validator$new(seed = 42)
Arguments
seedSet the instance variable
seed. (integer)
Returns
A new pgu.validator object.
(pguIMP::pgu.validator)
Method finalize()
Clears the heap and
indicates that instance of pgu.validator is removed from heap.
Usage
pgu.validator$finalize()
Method print()
Prints instance variables of a pgu.validator object.
Usage
pgu.validator$print()
Returns
string
Method resetValidator()
Resets instance variables
Usage
pgu.validator$resetValidator()
Method kolmogorowTestFeature()
Performs a comparison between the original and the imputated distribution of a given feature using a two-sided Kolmorogow-Smirnow test with simulated p-vaue distribution.
Usage
pgu.validator$kolmogorowTestFeature( org = "numeric", imp = "numeric", feature = "character" )
Arguments
orgOriginal data to be analzed. (numeric)
impImputed data to be analyzed. (numeric)
featureFeature name of the analyzed distributions. (character)
Returns
One row dataframe comprising the test results. (tibble::tibble)
Method wilcoxonTestFeature()
Performs a comparison between the original and the imputated distribution of a given feature using a two-sided Wilcoxon/Mann-Whitney test.
Usage
pgu.validator$wilcoxonTestFeature( org = "numeric", imp = "numeric", feature = "character" )
Arguments
orgOriginal data to be analzed. (numeric)
impImputed data to be analyzed. (numeric)
featureFeature name of the analyzed distributions. (character)
Returns
One row dataframe comprising the test results. (tibble::tibble)
Method centralMomentsFeature()
Estimates estimates the central moments of the given distribution.
Usage
pgu.validator$centralMomentsFeature(values = "numeric", feature = "character")
Arguments
valuesData to be analzed. (numeric)
featureFeature name of the analyzed distributions. (character)
Returns
One row dataframe comprising the statistics. (tibble::tibble)
Method validate()
Validates the feature distributions of the original and the imputated dataframe“
using a two-sided Kolmorogow-Smirnow test and a two-sided Wilcoxon/Mann-Whitney test.
The result is stored in the instance variables testStatistics_dfand 'distributionStatistics_df'.
Displays the progress if shiny is loaded.
Usage
pgu.validator$validate( org_df = "tbl_df", imp_df = "tbl_df", progress = "Progress" )
Arguments
org_dfOriginal dataframe to be analzed. (tibble::tibble)
imp_dfImputed dataframe to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
Method featurePdf()
Receives a dataframe and plots the pareto density of the features 'org_pdf' and 'imp_pdf'. Returns the plot
Usage
pgu.validator$featurePdf(data_df = "tbl_df")
Arguments
data_dfdataframe to be plotted (tibble::tibble)
Returns
A ggplot2 object (ggplot2::ggplot)
Method featureCdf()
Receives a dataframe and plost the feature 'x' against the features 'org_cdf' and 'imp_cdf'. Returns the plot
Usage
pgu.validator$featureCdf(data_df = "tbl_df")
Arguments
data_dfdataframe to be plotted (tibble::tibble)
Returns
A ggplot2 object (ggplot2::ggplot)
Method featureVs()
Receives two numeric vectors 'org' and 'imp'. Plots the qq-plot of both vectors. Returns the plot
Usage
pgu.validator$featureVs(org = "numeric", imp = "numeric")
Arguments
orgNumric vector comprising the original data. (numeric)
impNumeric vector comprising the imputed data. (numeric)
Returns
A ggplot2 object (ggplot2::ggplot)
Method featureBoxPlot()
Receives a dataframe and information about the lloq and uloq and retuns a boxplot
Usage
pgu.validator$featureBoxPlot( data_df = "tbl_df", lloq = "numeric", uloq = "numeric", feature = "character" )
Arguments
data_dfDataframe to be analyzed (tibble::tibble)
lloqlower limit of quantification (numeric)
uloqupper limit of quantification (numeric)
featureFeature name (character)
Returns
A ggplot2 object (ggplot2::ggplot)
Method featurePlot()
Receives two numeric dataframes 'org_df' and 'imp_df', and a feature name. Creates a compund plot of the validation results for the given feature.. Returns the plot
Usage
pgu.validator$featurePlot( org_df = "tbl_df", imp_df = "tbl_df", lloq = "numeric", uloq = "numeric", impIdx_df = "tbl_df", feature = "character" )
Arguments
org_dfDataframe comprising the original data. (tibble::tibble)
imp_dfDataframe comprising the imputed data. (tibble::tibble)
lloqlower limit of quantification (numeric)
uloqupper limit of quantification (numeric)
impIdx_dfdataframe comprising information about imputation sites (tibble::tibble)
featureFeature name. (character)
Returns
A ggplot2 object (ggplot2::ggplot)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.validator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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