pgu.imputation: pgu.imputation
In pguIMP: 'pguIMP'
Description
Format
Details
Active bindings
Methods
Author(s)
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
imputationStatisticsReturns the instance variable imputationStatistics.
(tibble::tibble)
imputationSitesReturns the instance variable imputationSites.
(tibble::tibble)
one_hot_dfReturns the positions of missings in one_hot encoding
(tibble::tibble)
imputationSiteDistributionReturns the instance variable imputationSiteDistribution.
(matrix)
imputationAgentAlphabetReturns the instance variable imputationagentAlphabet.
(character)
imputationAgentReturns the instance variable imputationAgent.
(character)
setImputationAgentSets the instance variable imputationAgent.
(character)
nNeighborsReturns the instance variable nNeighbors.
(integer)
setNNeighborsSets the instance variable nNeighbors.
(integer)
flux_dfReturns the instance variable flux_df
(tibble::tibble)
outflux_thrReturns the instance variable outflux_thr.
(numeric)
setOutflux_thrSets the instance variable outflux_thr.
(numeric)
pred_fracReturns the instance variable pred_frac.
(numeric)
setPred_fracSets the instance variable pred_frac.
(numeric)
pred_matReturns the instance variable pred_mat.
(matrix)
exclude_vecReturns the instance variable exclude_vec
(character)
seedReturns the instance variable seed.
(numeric)
setSeedSets the instance variable seed.
(numeric)
iterationsReturns the instance variable iterations.
(numeric)
setIterationsSets the instance variable iterations.
(numeric)
amvReturns the instance variable amv.
(numeric)
successReturns the instance variable success.
(logical)
Methods
Public methods
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Method new()
Creates and returns a new pgu.imputation object.
Usage
pgu.imputation$new(
seed = 42,
iterations = 4,
imputationAgent = "none",
nNeighbors = 3,
pred_frac = 1,
outflux_thr = 0.5
)
Arguments
seedInitially sets the instance variable seed.
Default is 42.
(integer)
iterationsInitially sets the instance variable iterations.
Default is 4.
(integer)
imputationAgentInitially sets the instance variable imputationAgent.
Default is "none".
Options are: ""none", "median", "mean", "expValue", "monteCarlo", "knn", "pmm", "cart", "randomForest", "M5P".
(string)
nNeighborsInitially sets the instance variable nNeighbors.
(integer)
pred_fracInitially sets the instance variable pred_frac.
(numeric)
outflux_thrInitially sets the instance fariable outflux_thr
Returns
A new pgu.imputation object.
(pguIMP::pgu.imputation)
Method finalize()
Clears the heap and
indicates that instance of pgu.imputation is removed from heap.
Usage
pgu.imputation$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
missings_dfDataframe comprising information about the imputation sites of pguIMP's missings class.
(tibble::tibble)
outliers_dfDataframe comprising information about the imputation sites of pguIMP's outliers class.
(tibble::tibble)
Method gatherImputationSiteStatistics()
Gathers statistical information about imputation sites
The information is stored within the classes instance variable imputationStatistics
Usage
pgu.imputation$gatherImputationSiteStatistics(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Method gatherImputationSiteDistribution()
Gathers the distribution of imputation sites within the data frame.
The information is stored within the classes instance variable imputationSiteDistribution.
Usage
pgu.imputation$gatherImputationSiteDistribution(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
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$insertImputationSites(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Returns
A mutated version of data_df.
(tibble::tibble)
Method one_hot()
Gathers statistical information about missing values
in one hot format.
The result is stored in the instance variable one_hot_df.
Usage
pgu.imputation$one_hot(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Method analyzeImputationSites()
Takes a dataframe and analyses the imputation sites.
Usage
pgu.imputation$analyzeImputationSites(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Method imputationSiteIdxByFeature()
Returns the position of an attribute's imputation sites within a data frame.
Usage
pgu.imputation$imputationSiteIdxByFeature(featureName = "character")
Arguments
featureNameThe attribute's name.
(character)
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$nanFeatureList(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Returns
Vector of row characteristics.
(character)
Method average_number_of_predictors()
Calculates the average number of predictors for a given dataframe and minpuc and mincor variables
using the mice::quickpred routine.
Usage
pgu.imputation$average_number_of_predictors(
data_df = "tbl_df",
minpuc = 0,
mincor = 0.1
)
Arguments
data_dfThe dataframe to be analyzed
(tibble::tibble)
minpucSpecifies the minimum threshold for the proportion of usable cases.
(numeric)
mincorSpecifies the minimum threshold against which the absolute correlation in the dataframe is compared.
(numeric)
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$detectPredictors(data_df = "tbl_df")
Arguments
data_dfThe dataframe to be analyzed.
(tibble::tibble)
Method handleImputationSites()
Chooses a cleaning method based upon the instance variable imputationAgent
and handles the imputation sites in the dataframe.
Returns a cleaned data set.
Display the progress if shiny is loaded.
Usage
pgu.imputation$handleImputationSites(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
(shiny::Progress)
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$imputeByMedian(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
Returns
Cleaned dataframe.
(tibble:tibble)
Method imputeByMean()
Substitutes imputation sites by the aritmertic mean of the respective attribute.
Returns the cleaned dataframe.
Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByMean(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
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$imputeByExpectationValue(
data_df = "tbl_df",
progress = "Progress"
)
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
Returns
Cleaned dataframe.
(tibble:tibble)
Method imputeByMC()
Substitutes imputation sites by values generated by a monte carlo simulation.
The procedure runs several times as defined by the instance variable iterations.
The run with the best result is identified and used for substitution.
Returns the cleaned dataframe.
Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByMC(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
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$imputeByKnn(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
Returns
Cleaned dataframe.
(tibble:tibble)
Method imputeByMice()
Substitutes imputation sites by values generated by a different methods of the mice package.
The procedure runs several times as defined by the instance variable iterations.
The run with the best result is identified and used for substitution.
Returns the cleaned dataframe.
Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByMice(data_df, progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
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$imputeByM5P(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
(shiny::Progress)
Returns
Cleaned dataframe.
(tibble:tibble)
Method imputationSiteHeatMap()
Displays the distribution of missing values in form of a heatmap.
Usage
pgu.imputation$imputationSiteHeatMap()
Returns
A heatmap plot.
(ggplot2::ggplot)
Method featureBarPlot()
Displays the distribution of an attribute values as histogram.
Usage
pgu.imputation$featureBarPlot(data_df = "tbl_df", feature = "character")
Arguments
data_dfdataframe to be analyzed.
(tibble::tibble)
featureattribute to be shown.
(character)
Returns
A histogram.
(ggplot2::ggplot)
Method featureBoxPlotWithSubset()
Displays the distribution of an attribute's values as box plot.
Usage
pgu.imputation$featureBoxPlotWithSubset(
data_df = "tbl_df",
feature = "character"
)
Arguments
data_dfdataframe to be analyzed.
(tibble::tibble)
featureattribute to be shown.
(character)
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$featurePlot(data_df = "tbl_df", feature = "character")
Arguments
data_dfdataframe to be analyzed.
(tibble::tibble)
featureattribute to be shown.
(character)
Returns
A composite plot.
(ggplot2::ggplot)
Method fluxPlot()
Displays an influx/outflux plot
Usage
pgu.imputation$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
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.
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Description Format Details Active bindings Methods Author(s)
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
imputationStatisticsReturns the instance variable imputationStatistics. (tibble::tibble)
imputationSitesReturns the instance variable imputationSites. (tibble::tibble)
one_hot_dfReturns the positions of missings in one_hot encoding (tibble::tibble)
imputationSiteDistributionReturns the instance variable imputationSiteDistribution. (matrix)
imputationAgentAlphabetReturns the instance variable imputationagentAlphabet. (character)
imputationAgentReturns the instance variable imputationAgent. (character)
setImputationAgentSets the instance variable imputationAgent. (character)
nNeighborsReturns the instance variable nNeighbors. (integer)
setNNeighborsSets the instance variable nNeighbors. (integer)
flux_dfReturns the instance variable flux_df (tibble::tibble)
outflux_thrReturns the instance variable outflux_thr. (numeric)
setOutflux_thrSets the instance variable outflux_thr. (numeric)
pred_fracReturns the instance variable pred_frac. (numeric)
setPred_fracSets the instance variable pred_frac. (numeric)
pred_matReturns the instance variable pred_mat. (matrix)
exclude_vecReturns the instance variable exclude_vec (character)
seedReturns the instance variable seed. (numeric)
setSeedSets the instance variable seed. (numeric)
iterationsReturns the instance variable iterations. (numeric)
setIterationsSets the instance variable iterations. (numeric)
amvReturns the instance variable amv. (numeric)
successReturns the instance variable success. (logical)
Methods
Public methods
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
seedInitially sets the instance variable seed. Default is 42. (integer)
iterationsInitially sets the instance variable iterations. Default is 4. (integer)
imputationAgentInitially sets the instance variable imputationAgent. Default is "none". Options are: ""none", "median", "mean", "expValue", "monteCarlo", "knn", "pmm", "cart", "randomForest", "M5P". (string)
nNeighborsInitially sets the instance variable nNeighbors. (integer)
pred_fracInitially sets the instance variable pred_frac. (numeric)
outflux_thrInitially sets the instance fariable outflux_thr
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$gatherImputationSites( missings_df = "tbl_df", outliers_df = "tbl_df" )
Arguments
missings_dfDataframe comprising information about the imputation sites of pguIMP's missings class. (tibble::tibble)
outliers_dfDataframe comprising information about the imputation sites of pguIMP's outliers class. (tibble::tibble)
Method gatherImputationSiteStatistics()
Gathers statistical information about imputation sites
The information is stored within the classes instance variable imputationStatistics
Usage
pgu.imputation$gatherImputationSiteStatistics(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
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$gatherImputationSiteDistribution(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Returns
A data frame (tibble::tibble)
Method insertImputationSites()
Takes a dataframe, replaces the imputation sites indicated by the instance variable imputationsites by NA,
and returns the mutated dataframe.
Usage
pgu.imputation$insertImputationSites(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
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$one_hot(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Method analyzeImputationSites()
Takes a dataframe and analyses the imputation sites.
Usage
pgu.imputation$analyzeImputationSites(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Method imputationSiteIdxByFeature()
Returns the position of an attribute's imputation sites within a data frame.
Usage
pgu.imputation$imputationSiteIdxByFeature(featureName = "character")
Arguments
featureNameThe attribute's name. (character)
Returns
The postion of the imputation sites. (numeric)
Method nanFeatureList()
Characterizes each row of the data frame as either complete
or indicates which attribute are missing within the row.
If multiple attributes' row entries are missing, the row is characterized by multiple.
Usage
pgu.imputation$nanFeatureList(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
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$average_number_of_predictors( data_df = "tbl_df", minpuc = 0, mincor = 0.1 )
Arguments
data_dfThe dataframe to be analyzed (tibble::tibble)
minpucSpecifies the minimum threshold for the proportion of usable cases. (numeric)
mincorSpecifies the minimum threshold against which the absolute correlation in the dataframe is compared. (numeric)
Returns
Average_number_of_predictors. (numeric)
Method detectPredictors()
Identifies possible predictors for each feature. Analysis results are written to the instance variable pred_mat. Intermediate analysis results are an influx/outflux dataframe that is written to the instance variable flux_df and detect predictors and a list of features that is excluded from the search for possible predictors that is written to the instance variable exclude_vec.
Usage
pgu.imputation$detectPredictors(data_df = "tbl_df")
Arguments
data_dfThe dataframe to be analyzed. (tibble::tibble)
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$handleImputationSites(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class. (shiny::Progress)
Returns
Cleaned dataframe. (tibble:tibble)
Method imputeByMedian()
Substitutes imputation sites by the median of the respective attribute. Returns the cleaned dataframe. Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByMedian(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
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$imputeByMean(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
Returns
Cleaned dataframe. (tibble:tibble)
Method imputeByExpectationValue()
Substitutes imputation sites by the expectation value of the respective attribute. Returns the cleaned dataframe. Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByExpectationValue( data_df = "tbl_df", progress = "Progress" )
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
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$imputeByMC(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
Returns
Cleaned dataframe. (tibble:tibble)
Method imputeByKnn()
Substitutes imputation sites by predictions of a KNN analysis of the whole dataframe. Returns the cleaned dataframe. Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByKnn(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
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$imputeByMice(data_df, progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
Returns
Cleaned dataframe. (tibble:tibble)
Method imputeByM5P()
Substitutes imputation sites by predictions of a M5P tree trained on the whole dataframe. Returns the cleaned dataframe. Display the progress if shiny is loaded.
Usage
pgu.imputation$imputeByM5P(data_df = "tbl_df", progress = "Progress")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class. (shiny::Progress)
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
data_dfdataframe to be analyzed. (tibble::tibble)
featureattribute to be shown. (character)
Returns
A histogram. (ggplot2::ggplot)
Method featureBoxPlotWithSubset()
Displays the distribution of an attribute's values as box plot.
Usage
pgu.imputation$featureBoxPlotWithSubset( data_df = "tbl_df", feature = "character" )
Arguments
data_dfdataframe to be analyzed. (tibble::tibble)
featureattribute to be shown. (character)
Returns
A box plot. (ggplot2::ggplot)
Method featurePlot()
Displays the distribution of an attribute's values as a composition of a box plot and a histogram.
Usage
pgu.imputation$featurePlot(data_df = "tbl_df", feature = "character")
Arguments
data_dfdataframe to be analyzed. (tibble::tibble)
featureattribute to be shown. (character)
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
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
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