pgu.optimizer: pgu.optimizer
In pguIMP: 'pguIMP'
Description
Format
Details
Active bindings
Methods
Author(s)
Description
Finds the transformation models that result in distributions that come closest to a normal distribution.
Format
R6::R6Class object.
Details
Analysis is performed individually on each attribute.
This object is used by the shiny based gui and is not for use in individual R-scripts!
Active bindings
featuresReturns the instance variable features.
(character)
trafoAlphabetReturns the instance variable trafoAlphabet.
(character)
setTrafoAlphabetSets the instance variable trafoAlphabet to data.
(character)
mirrorReturns the instance variable mirror
(logical)
setMirrorSets the instance variable mirror to data
(logical)
optParameterReturns the instance variable optParameter
(tibble::tibble)
optTypesReturns the instance variable optTypes
(tibble::tibble)
Methods
Public methods
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Method new()
Creates and returns a new pgu.optimizer object.
Usage
pgu.optimizer$new(data = "tbl_df")
Arguments
dataThe data to be analyzed.
(tibble::tibble)
Returns
A new pgu.optimizer object.
(pguIMP::pgu.optimizer)
Method finalize()
Clears the heap and
indicates that instance of pgu.optimizer is removed from heap.
Usage
pgu.optimizer$finalize()
Method print()
Prints instance variables of a pgu.optimizer object.
Usage
pgu.optimizer$print()
Returns
string
Method resetFeatures()
Extract the attribute names from the given data frame
and stores them in the class' instance variable
features,
Usage
pgu.optimizer$resetFeatures(data = "tbl_df")
Arguments
dataThe data to be analyzed.
(tibble::tibble)
Method resetOptParameter()
Initializes the instance variable optParameter.
Usage
pgu.optimizer$resetOptParameter()
Method resetOptTypes()
Initializes the instance variable optTypes.
Usage
pgu.optimizer$resetOptTypes()
Method resetOptimizer()
Initializes the optimizer instance variables.
Here, initialization defines a consecutive sequence of the class' functions:
resetFeatures, setTrafoAlphabet, setMirror, resetOptParameter and resetOptTypes.
Usage
pgu.optimizer$resetOptimizer(data = "tbl_df")
Arguments
dataThe data to be analyzed.
(tibble::tibble)
Method featureIdx()
Determines the numerical index of the column of an attribute based on the attribute name.
Usage
pgu.optimizer$featureIdx(feature = "character")
Arguments
featureThe attribute's name.
(character)
Returns
The attributes column index.
(numeric)
Method modelParameterIsBigger()
Compares a model parameter to a reference parameter and tests, if the model parameter is bigger.
Usage
pgu.optimizer$modelParameterIsBigger(
modelParameter = "numeric",
referenceParameter = "numeric"
)
Arguments
modelParameterThe model parameter
(numeric)
referenceParameterThe reference parameter
(numeric)
Returns
Test Result
(logical)
Method modelParameterIsSmaller()
Compares a model parameter to a reference parameter and tests, if the model parameter is smaller.
Usage
pgu.optimizer$modelParameterIsSmaller(
modelParameter = "numeric",
referenceParameter = "numeric"
)
Arguments
modelParameterThe model parameter
(numeric)
referenceParameterThe reference parameter
(numeric)
Returns
Test Result
(logical)
Method updateTrafoType()
Takes an instance of the pgu.transfromator class and
sets the transformation type to a user defined value.
Usage
pgu.optimizer$updateTrafoType(
transformator = "pgu.transformator",
type = "character"
)
Arguments
transformatorAn instance of the pgu.transformator class
(pguIMP::pgu.transformator)
typeA transfromation type
(character)
Returns
An updated instance of the pgu.transformator class
(pguIMP::pgu.transformator)
Method updateMirrorLogic()
Takes an instance of the pgu.transfromator class and
sets the mirrorLogic parameter to a user defined value.
Usage
pgu.optimizer$updateMirrorLogic(
transformator = "pgu.transformator",
logic = "logical"
)
Arguments
transformatorAn instance of the pgu.transformator class
(pguIMP::pgu.transformator)
logicThe mirrorLogic parameter
(logic)
Returns
An updated instance of the pgu.transformator class
(pguIMP::pgu.transformator)
Method updateOptParameter()
Takes an instance of the pgu.model class and analyzes it.
Keeps track of the optimal model parameters during optimization
and stores them in the instance variables optTypes and optParameter.
Usage
pgu.optimizer$updateOptParameter(
model = "pgu.model",
type = "character",
logic = "character"
)
Arguments
modelAn instance of the pgu.model class
(pguIMP::pgu.model)
typeA transfromation type
(character)
logicThe mirrorLogic parameter
(logic)
Method optimize()
Permutates all possible variations of data transfromations and iterates through them.
Analysis the optimal transformation parameters for each attribute in the data frame and stores them
in the instance variables optParameter, optTypes.
Usage
pgu.optimizer$optimize(data = "tbl_df", progress = "Progress")
Arguments
dataThe 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)
Method trafoAlpahbetTblDf()
Returns information on the optimization progress
Usage
pgu.optimizer$trafoAlpahbetTblDf()
Returns
The data frame comprizing analysis information.
(tibble::tibble)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.optimizer$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
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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
Finds the transformation models that result in distributions that come closest to a normal distribution.
Format
R6::R6Class object.
Details
Analysis is performed individually on each attribute. This object is used by the shiny based gui and is not for use in individual R-scripts!
Active bindings
featuresReturns the instance variable features. (character)
trafoAlphabetReturns the instance variable trafoAlphabet. (character)
setTrafoAlphabetSets the instance variable trafoAlphabet to data. (character)
mirrorReturns the instance variable mirror (logical)
setMirrorSets the instance variable mirror to data (logical)
optParameterReturns the instance variable optParameter (tibble::tibble)
optTypesReturns the instance variable optTypes (tibble::tibble)
Methods
Public methods
Method new()
Creates and returns a new pgu.optimizer object.
Usage
pgu.optimizer$new(data = "tbl_df")
Arguments
dataThe data to be analyzed. (tibble::tibble)
Returns
A new pgu.optimizer object.
(pguIMP::pgu.optimizer)
Method finalize()
Clears the heap and
indicates that instance of pgu.optimizer is removed from heap.
Usage
pgu.optimizer$finalize()
Method print()
Prints instance variables of a pgu.optimizer object.
Usage
pgu.optimizer$print()
Returns
string
Method resetFeatures()
Extract the attribute names from the given data frame and stores them in the class' instance variable features,
Usage
pgu.optimizer$resetFeatures(data = "tbl_df")
Arguments
dataThe data to be analyzed. (tibble::tibble)
Method resetOptParameter()
Initializes the instance variable optParameter.
Usage
pgu.optimizer$resetOptParameter()
Method resetOptTypes()
Initializes the instance variable optTypes.
Usage
pgu.optimizer$resetOptTypes()
Method resetOptimizer()
Initializes the optimizer instance variables. Here, initialization defines a consecutive sequence of the class' functions: resetFeatures, setTrafoAlphabet, setMirror, resetOptParameter and resetOptTypes.
Usage
pgu.optimizer$resetOptimizer(data = "tbl_df")
Arguments
dataThe data to be analyzed. (tibble::tibble)
Method featureIdx()
Determines the numerical index of the column of an attribute based on the attribute name.
Usage
pgu.optimizer$featureIdx(feature = "character")
Arguments
featureThe attribute's name. (character)
Returns
The attributes column index. (numeric)
Method modelParameterIsBigger()
Compares a model parameter to a reference parameter and tests, if the model parameter is bigger.
Usage
pgu.optimizer$modelParameterIsBigger( modelParameter = "numeric", referenceParameter = "numeric" )
Arguments
modelParameterThe model parameter (numeric)
referenceParameterThe reference parameter (numeric)
Returns
Test Result (logical)
Method modelParameterIsSmaller()
Compares a model parameter to a reference parameter and tests, if the model parameter is smaller.
Usage
pgu.optimizer$modelParameterIsSmaller( modelParameter = "numeric", referenceParameter = "numeric" )
Arguments
modelParameterThe model parameter (numeric)
referenceParameterThe reference parameter (numeric)
Returns
Test Result (logical)
Method updateTrafoType()
Takes an instance of the pgu.transfromator class and sets the transformation type to a user defined value.
Usage
pgu.optimizer$updateTrafoType( transformator = "pgu.transformator", type = "character" )
Arguments
transformatorAn instance of the pgu.transformator class (pguIMP::pgu.transformator)
typeA transfromation type (character)
Returns
An updated instance of the pgu.transformator class (pguIMP::pgu.transformator)
Method updateMirrorLogic()
Takes an instance of the pgu.transfromator class and sets the mirrorLogic parameter to a user defined value.
Usage
pgu.optimizer$updateMirrorLogic( transformator = "pgu.transformator", logic = "logical" )
Arguments
transformatorAn instance of the pgu.transformator class (pguIMP::pgu.transformator)
logicThe mirrorLogic parameter (logic)
Returns
An updated instance of the pgu.transformator class (pguIMP::pgu.transformator)
Method updateOptParameter()
Takes an instance of the pgu.model class and analyzes it. Keeps track of the optimal model parameters during optimization and stores them in the instance variables optTypes and optParameter.
Usage
pgu.optimizer$updateOptParameter( model = "pgu.model", type = "character", logic = "character" )
Arguments
modelAn instance of the pgu.model class (pguIMP::pgu.model)
typeA transfromation type (character)
logicThe mirrorLogic parameter (logic)
Method optimize()
Permutates all possible variations of data transfromations and iterates through them. Analysis the optimal transformation parameters for each attribute in the data frame and stores them in the instance variables optParameter, optTypes.
Usage
pgu.optimizer$optimize(data = "tbl_df", progress = "Progress")
Arguments
dataThe 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)
Method trafoAlpahbetTblDf()
Returns information on the optimization progress
Usage
pgu.optimizer$trafoAlpahbetTblDf()
Returns
The data frame comprizing analysis information. (tibble::tibble)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.optimizer$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
R Package Documentation
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