pgu.regressor: pgu.regressor
In pguIMP: 'pguIMP'
Description
Format
Details
Active bindings
Methods
Author(s)
Description
A class that performs pairwise robust regression on the pguIMP data set.
Format
R6::R6Class object.
Details
A class that performs pairwise robust regression on the pguIMP data set.
This object is used by the shiny based gui and is not for use in individual R-scripts!
Active bindings
featureNamesReturns the instance variable featureNames.
(character)
setFeatureNamesSets the instance variable featureNames.
It further initializes the instance variables:
intercept, pIntercept, slope, pSlope.
(character)
interceptReturns the instance variable intercept.
(matrix)
pInterceptReturns instance variable pIntercept.
(matrix)
slopeReturns the instance variable slope.
(matrix)
pSlopeReturns the instance variable pSlope.
(matrix)
modelReturns the instance variable model.
(robust::lmRob)
Methods
Public methods
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Method new()
Creates and returns a new pgu.regressor object.
Usage
pgu.regressor$new(data = "tbl_df")
Arguments
dataThe data to be modeled.
(tibble::tibble)
Returns
A new pgu.regressor object.
(pguIMP::pgu.regressor)
Method finalize()
Clears the heap and
indicates if instance of pgu.regressor is removed from heap.
Usage
pgu.regressor$finalize()
Method print()
Prints instance variables of a pgu.regressor object.
Usage
pgu.regressor$print()
Returns
string
Method resetRegressor()
Performes pair-wise robust linear regression on the attributes of the data tibble.
Progresse is indicated by the progress object passed to the function.
Usage
pgu.regressor$resetRegressor(data = "tbl_df", progress = "Progress")
Arguments
dataDataframe with at least two numeric attributes.
(tibble::tibble)
progressKeeps track of the analysis progress.
(shiny::Progress)
Method resetDiagonal()
Sets the diagonal of a square matrix to NA.
Usage
pgu.regressor$resetDiagonal(data = "matrix")
Arguments
dataThe matrix whose diagonal is to be reset.
(matrix)
Returns
A matrix with its diagonal reset to NA.
(matrix)
Method resetMatrix()
Creates a square matrix which dimension corresponds to the length
of the instance variable featureNames. The matrix entries are set to a distict value.
The diagonal is set to NA.
Usage
pgu.regressor$resetMatrix(value = "numeric")
Arguments
valueThe value the matrix entries are set to.
(numeric)
Returns
A square matrix.
(matrix)
Method featureIdx()
Determines the numerical index of the column of an attribute based on the attribute name.
Usage
pgu.regressor$featureIdx(feature = "character")
Arguments
featureThe attribute's name.
(character)
Returns
The attributes column index.
(numeric)
Method featureIsValid()
Checks if the feature is known to the class.
Usage
pgu.regressor$featureIsValid(feature = "character")
Arguments
featureAn attribute's name that is to be checked.
(character)
Returns
The test result.
(logical)
Method featurePairIsValid()
Checks a if a pair of attributes different and known to the class.
Usage
pgu.regressor$featurePairIsValid(
abscissa = "character",
ordinate = "character"
)
Arguments
abscissaAn attribute's name that is to be checked.
(character)
ordinateAn attribute's name that is to be checked.
(character)
Returns
The test result.
(logical)
Method createModel()
Creates a robust model of linear regression between two attributes of a dataframe.
The model is stored as instance variable.
Usage
pgu.regressor$createModel(
data = "tbl_df",
abscissa = "character",
ordinate = "character"
)
Arguments
dataThe data to be modeled.
(tibble::tibble)
abscissaAn attribute's name that equals a column name in the data.
(character)
ordinateAn attribute's name that equals a column name in the data.
(character)
Method createRegressionMatrix()
Performs the actual robust linear regression routine.
Iteratively runs through the attributes known to the class
and creates a robust linear regression model for each valid attribute pair.
The model results are stored in the instance variables:
intercept, pIntercept, slope, pSlope.
Here, pX represents the p-value of the respective parameter X.
Displays the progress if shiny is loaded.
Usage
pgu.regressor$createRegressionMatrix(data = "tbl_df", progress = "Progress")
Arguments
dataThe data to be modeled.
(tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
(shiny::Progress)
Method printModel()
Transforms the results of the modeling procedure for a valid pair of attributes to a dataframe
and returns it.
Usage
pgu.regressor$printModel(abscissa = "character", ordinate = "character")
Arguments
abscissaThe name of the attribute which is assigned to the abscissa.
(character)
ordinateThe name of the attribute which is assigned to the ordinate.
(character)
Returns
The analyis result as a dataframe.
(tibble::tibble)
Method printInterceptTbl()
Transfroms instance variable intercept to a dataframe and returns it.
Usage
pgu.regressor$printInterceptTbl()
Returns
Dataframe of instance variable intercept.
(tibble::tibble)
Method printPInterceptTbl()
Transfroms instance variable pIntercept to a dataframe and returns it.
Usage
pgu.regressor$printPInterceptTbl()
Returns
Dataframe of instance variable pIntercept.
(tibble::tibble)
Method printSlopeTbl()
Transfroms instance variable slope to a dataframe and returns it.
Usage
pgu.regressor$printSlopeTbl()
Returns
Dataframe of instance variable slope.
(tibble::tibble)
Method printPSlopeTbl()
Transfroms instance variable pSlope to a dataframe and returns it.
Usage
pgu.regressor$printPSlopeTbl()
Returns
Dataframe of instance variable pSlope.
(tibble::tibble)
Method plotRegression()
Creates a scatter plot of the model
stored within the instance variable of the class.
Usage
pgu.regressor$plotRegression()
Returns
A scatter plot.
(ggplot2::ggplot)
Method plotResidualDist()
Creates a histogram of the residual distribution of the model
stored within the instance variable of the class.
Usage
pgu.regressor$plotResidualDist()
Returns
A histogram plot.
(ggplot2::ggplot)
Method plotResidualBox()
Creates a box plot of the residual distribution of the model
stored within the instance variable of the class.
Usage
pgu.regressor$plotResidualBox()
Returns
A box plot.
(ggplot2::ggplot)
Method plotModel()
Creates a model of robust linear regression.
Executes all graphical exploration functions of the class
and creates a composite graph based on their results.
Usage
pgu.regressor$plotModel(
data = "tbl_df",
abscissa = "character",
ordinate = "character"
)
Arguments
dataThe data to be modeled.
(tibble::tibble)
abscissaThe name of the attribute which is assigned to the abscissa.
(character)
ordinateThe name of the attribute which is assigned to the ordinate.
(character)
Returns
A composite graph.
(gridExtra::grid.arrange)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.regressor$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
pguIMP documentation built on Sept. 30, 2021, 5:08 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Format Details Active bindings Methods Author(s)
Description
A class that performs pairwise robust regression on the pguIMP data set.
Format
R6::R6Class object.
Details
A class that performs pairwise robust regression on the pguIMP data set. This object is used by the shiny based gui and is not for use in individual R-scripts!
Active bindings
featureNamesReturns the instance variable featureNames. (character)
setFeatureNamesSets the instance variable featureNames. It further initializes the instance variables: intercept, pIntercept, slope, pSlope. (character)
interceptReturns the instance variable intercept. (matrix)
pInterceptReturns instance variable pIntercept. (matrix)
slopeReturns the instance variable slope. (matrix)
pSlopeReturns the instance variable pSlope. (matrix)
modelReturns the instance variable model. (robust::lmRob)
Methods
Public methods
Method new()
Creates and returns a new pgu.regressor object.
Usage
pgu.regressor$new(data = "tbl_df")
Arguments
dataThe data to be modeled. (tibble::tibble)
Returns
A new pgu.regressor object.
(pguIMP::pgu.regressor)
Method finalize()
Clears the heap and
indicates if instance of pgu.regressor is removed from heap.
Usage
pgu.regressor$finalize()
Method print()
Prints instance variables of a pgu.regressor object.
Usage
pgu.regressor$print()
Returns
string
Method resetRegressor()
Performes pair-wise robust linear regression on the attributes of the data tibble. Progresse is indicated by the progress object passed to the function.
Usage
pgu.regressor$resetRegressor(data = "tbl_df", progress = "Progress")
Arguments
dataDataframe with at least two numeric attributes. (tibble::tibble)
progressKeeps track of the analysis progress. (shiny::Progress)
Method resetDiagonal()
Sets the diagonal of a square matrix to NA.
Usage
pgu.regressor$resetDiagonal(data = "matrix")
Arguments
dataThe matrix whose diagonal is to be reset. (matrix)
Returns
A matrix with its diagonal reset to NA. (matrix)
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.
The diagonal is set to NA.
Usage
pgu.regressor$resetMatrix(value = "numeric")
Arguments
valueThe value the matrix entries are set to. (numeric)
Returns
A square matrix. (matrix)
Method featureIdx()
Determines the numerical index of the column of an attribute based on the attribute name.
Usage
pgu.regressor$featureIdx(feature = "character")
Arguments
featureThe attribute's name. (character)
Returns
The attributes column index. (numeric)
Method featureIsValid()
Checks if the feature is known to the class.
Usage
pgu.regressor$featureIsValid(feature = "character")
Arguments
featureAn attribute's name that is to be checked. (character)
Returns
The test result. (logical)
Method featurePairIsValid()
Checks a if a pair of attributes different and known to the class.
Usage
pgu.regressor$featurePairIsValid( abscissa = "character", ordinate = "character" )
Arguments
abscissaAn attribute's name that is to be checked. (character)
ordinateAn attribute's name that is to be checked. (character)
Returns
The test result. (logical)
Method createModel()
Creates a robust model of linear regression between two attributes of a dataframe. The model is stored as instance variable.
Usage
pgu.regressor$createModel( data = "tbl_df", abscissa = "character", ordinate = "character" )
Arguments
dataThe data to be modeled. (tibble::tibble)
abscissaAn attribute's name that equals a column name in the data. (character)
ordinateAn attribute's name that equals a column name in the data. (character)
Method createRegressionMatrix()
Performs the actual robust linear regression routine. Iteratively runs through the attributes known to the class and creates a robust linear regression model for each valid attribute pair. The model results are stored in the instance variables: intercept, pIntercept, slope, pSlope. Here, pX represents the p-value of the respective parameter X. Displays the progress if shiny is loaded.
Usage
pgu.regressor$createRegressionMatrix(data = "tbl_df", progress = "Progress")
Arguments
dataThe data to be modeled. (tibble::tibble)
progressIf shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class. (shiny::Progress)
Method printModel()
Transforms the results of the modeling procedure for a valid pair of attributes to a dataframe and returns it.
Usage
pgu.regressor$printModel(abscissa = "character", ordinate = "character")
Arguments
abscissaThe name of the attribute which is assigned to the abscissa. (character)
ordinateThe name of the attribute which is assigned to the ordinate. (character)
Returns
The analyis result as a dataframe. (tibble::tibble)
Method printInterceptTbl()
Transfroms instance variable intercept to a dataframe and returns it.
Usage
pgu.regressor$printInterceptTbl()
Returns
Dataframe of instance variable intercept. (tibble::tibble)
Method printPInterceptTbl()
Transfroms instance variable pIntercept to a dataframe and returns it.
Usage
pgu.regressor$printPInterceptTbl()
Returns
Dataframe of instance variable pIntercept. (tibble::tibble)
Method printSlopeTbl()
Transfroms instance variable slope to a dataframe and returns it.
Usage
pgu.regressor$printSlopeTbl()
Returns
Dataframe of instance variable slope. (tibble::tibble)
Method printPSlopeTbl()
Transfroms instance variable pSlope to a dataframe and returns it.
Usage
pgu.regressor$printPSlopeTbl()
Returns
Dataframe of instance variable pSlope. (tibble::tibble)
Method plotRegression()
Creates a scatter plot of the model stored within the instance variable of the class.
Usage
pgu.regressor$plotRegression()
Returns
A scatter plot. (ggplot2::ggplot)
Method plotResidualDist()
Creates a histogram of the residual distribution of the model stored within the instance variable of the class.
Usage
pgu.regressor$plotResidualDist()
Returns
A histogram plot. (ggplot2::ggplot)
Method plotResidualBox()
Creates a box plot of the residual distribution of the model stored within the instance variable of the class.
Usage
pgu.regressor$plotResidualBox()
Returns
A box plot. (ggplot2::ggplot)
Method plotModel()
Creates a model of robust linear regression. Executes all graphical exploration functions of the class and creates a composite graph based on their results.
Usage
pgu.regressor$plotModel( data = "tbl_df", abscissa = "character", ordinate = "character" )
Arguments
dataThe data to be modeled. (tibble::tibble)
abscissaThe name of the attribute which is assigned to the abscissa. (character)
ordinateThe name of the attribute which is assigned to the ordinate. (character)
Returns
A composite graph. (gridExtra::grid.arrange)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.regressor$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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