pgu.limitsOfQuantification: pgu.limitsOfQuantification
In pguIMP: 'pguIMP'
Description
Format
Details
Active bindings
Methods
Author(s)
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
loqReturns the instance variable loq
(tibble::tibble)
setLoqSets the instance variable loq.
(tibble::tibble)
outliersReturns instance variable outliers
(tibble::tibble)
lloqSubstituteAlphabetReturns the instance variable lloqSubstititeAlphabet
(character)
lloqSubstituteAgentReturns the instance variable lloqSubstituteAgent
(character)
setLloqSubstituteAgentSets the instance variable lloqSubstituteAgent.
(character)
uloqSubstituteAlphabetReturns the instance variable uloqSubstititeAlphabet
(character)
uloqSubstituteAgentReturns the instance variable uloqSubstituteAgent
(character)
setUloqSubstituteAgentSets the instance variable uloqSubstituteAgent.
(character)
naHandlingAlphabetReturns the instance variable naHandlingAlphabet
(character)
naHandlingAgentReturns the instance variable naHandlingAgentt
(character)
setNaHandlingAgentSets the instance variable naHandlingAgentt
(character)
loqStatisticsReturns the instance variable loqStatistics
Methods
Public methods
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Method new()
Mutates outlier candidates characterized as below LLOQ based on user defined actions.
Mutates outlier candidates characterized as above ULOQ based on user defined actions.
Searches for outliers in the given data frame.
If an outlier was found, it is appended to the instance variable outliers.
Indicates if an outlier was found.
Extends the instance variable outliers by one entry.
Tests if the provided attributes are known to the class.
Resets the class' instance variable outliers
Calculates statistics of outlier appearance.
Stores it into the instance variable loqStatistics
Resets the class' instance variable loqStatistics
Resets the class by a data frame comprising information about LOQs.
Resets the class by a vector of attribute names.
The Attributes' LOQs are initially assigned to na.
Clears the heap and
indicates that instance of pgu.limitaOfQuantification is removed from heap.
Creates and returns a new pgu.limitsOfQuantification object.
Usage
pgu.limitsOfQuantification$new(attribute_names = "character")
Arguments
attribute_namesVector of attribute names with to be analyzed by the loq object.
(character)
Returns
A new pgu.limitsOfQuantification object.
(pguIMP::pgu.limitsOfQuantification)
Method print()
Prints instance variables of a pgu.limitsOfQuantification object.
Usage
pgu.limitsOfQuantification$print()
Returns
string
Method reset()
Resets the pguIMP::pgu.limitsOfQuantification object
on the given parameters attribute_names and data_df
Usage
pgu.limitsOfQuantification$reset(
attribute_names = "character",
data_df = "tbl_df"
)
Arguments
attribute_namesVector of attribute names with to be analyzed by the loq object.
(character)
data_dfDataframe comprising loq information.
Feature names need to be 'attribute', 'LLOQ' and 'ULOQ'.
(tibble::tibble)
Method fit()
Analyses the data dets for instances outside of the LOQ defined value interval.
Usage
pgu.limitsOfQuantification$fit(data_df = "tbl_df")
Arguments
data_dfDataframe to be analyzed
Method predict()
Mutates all outlier candidates based on user defined actions.
Usage
pgu.limitsOfQuantification$predict(data_df = "tbl_df")
Arguments
data_dfThe data to be analyzed.
(tibble::tibble)
Returns
The revised data frame
(tibble::tibble)
Method attribute_lloq()
Returns the attribute's specific lloq.
Usage
pgu.limitsOfQuantification$attribute_lloq(attribute = "character")
Arguments
attributeThe attribute to be analyzed
(character)
Returns
The attribute's lloq
(numeric)
Method attribute_uloq()
Returns the attribute's specific uloq.
Usage
pgu.limitsOfQuantification$attribute_uloq(attribute = "character")
Arguments
attributeThe attribute to be analyzed
(character)
Returns
The attribute's uloq
(numeric)
Method set_attribute_lloq()
sets the attribute's specific lloq to value.
Usage
pgu.limitsOfQuantification$set_attribute_lloq(
attribute = "character",
value = NA
)
Arguments
attributeThe attribute to be updated
(character)
valueThe value parsed to the attributes lloq
(numeric)
Method set_attribute_uloq()
sets the attribute's specific uloq to value.
Usage
pgu.limitsOfQuantification$set_attribute_uloq(
attribute = "character",
value = NA
)
Arguments
attributeThe attribute to be updated
(character)
valueThe value parsed to the attributes lloq
(numeric)
Method attribute_outliers()
Returns the detected outliers of a given attribute.
Usage
pgu.limitsOfQuantification$attribute_outliers(attribute = "character")
Arguments
attributeThe attribute to be analyzed
(character)
Returns
The attribute's outliers
(tibble::tibble)
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####################
# data information #
####################
#' @description
#' Gathers and returns class information
dataInformation = function(){
self$loq %>%
dplyr::summarise_all(class) %>%
tidyr::gather(variable, class) %>%
return()
}, #function
####################
# output functions #
####################
#' @description
#' Merges dfData and dfMetadata and returns a fromatted data table.
#' @param dfData
#' The data to be analyzed.
#' (tibble::tibble)
#' @param dfMetadata
#' The data frame containing metadata.
#' (tibble::tibble)
#' @return
#' A formatted data table
#' (DT::datatable)
loqDataTable = function(dfData = "tbl_df", dfMetadata = "tbl_df"){
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
t <- NULL
featureNames <- colnames(dfData)
tryCatch(
dfMerge <- dplyr::bind_cols(dfMetadata, dfData),
error = function(e){
print("error")
print(e)
dfMerge <- dfData
}#error
)#tryCatch
if(self$checkValidity(featureNames)){
t <- dfMerge %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
DT::datatable(options = list(scrollX = TRUE,
scrollY = '350px',
paging = FALSE))
for (featureName in featureNames){
featureOutlier <- self$outliers %>%
dplyr::filter(feature == featureName) %>%
dplyr::mutate_if(is.numeric, round, 3)
if (nrow(featureOutlier) > 0){
t <- DT::formatStyle(t,
featureName,
backgroundColor = DT::styleEqual(dfMerge %>%
dplyr::select(!!featureName) %>%
dplyr::slice(featureOutlier[["measurement"]]) %>%
unlist() %>%
round(digits = 3),
featureOutlier[["color"]]))
}#if
}#for
}#if
return(t)
}, #function
#' @description
#' Returns a formatted data table with comrising the information of a user defined attribute's outliers.
#' @param obj
#' The data to be analyzed.
#' (tibble::tibble)
#' @param feature
#' The attribute to be analyzed
#' (character)
#' @return
#' A formatted data table
#' (DT::datatable)
loqFeatureTable = function(obj = "tbl_df", feature = "character"){
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
t <- NULL
if(self$checkValidity(feature)){
featureOutlier <- self$outliers %>%
dplyr::filter(feature == !!feature) %>%
dplyr::mutate_if(is.numeric, round, 3)
dfFeature <- obj %>%
dplyr::mutate_if(is.numeric, round, 3)
print(dfFeature)
t <- dfFeature %>%
DT::datatable(options = list(scrollX = TRUE,
scrollY = '350px',
paging = FALSE))
if (nrow(featureOutlier) > 0){
t <- DT::formatStyle(
t,
feature,
backgroundColor = DT::styleEqual(dfFeature %>%
dplyr::select(!!feature) %>%
dplyr::slice(featureOutlier[["measurement"]]) %>%
unlist() %>%
round(digits = 3),
featureOutlier[["color"]]))
}#if
}#if
return(t)
}, #function
Method plot_loq_distribution()
Creates a plot of the instance variable loqStatistics.
Usage
pgu.limitsOfQuantification$plot_loq_distribution()
Returns
A plot.
(ggplot2::ggplot)
Method attribute_bar_plot()
Creates a bar plot of a user defined attribute's value distribution.
LOQs are indicated as dotted lines
Usage
pgu.limitsOfQuantification$attribute_bar_plot(
data_df = "tbl_df",
attribute = "character"
)
Arguments
data_dfThe data to be analyzed.
(tibble::tibble)
attributeThe attribute to be analyzed
(character)
Returns
A bar plot.
(ggplot2::ggplot)
Method attribute_box_plot_with_subset()
Creates a box plot of a user defined attribute's value distribution.
LOQs are indicated as dotted lines
Usage
pgu.limitsOfQuantification$attribute_box_plot_with_subset(
data_df = "tbl_df",
attribute = "character"
)
Arguments
data_dfThe data to be analyzed.
(tibble::tibble)
attributeThe attribute to be analyzed
(character)
Returns
A box plot.
(ggplot2::ggplot)
Method attribute_plot()
Creates and returns a composite graphical analysis
of the outlier analysis of a user defined attribute.
Usage
pgu.limitsOfQuantification$attribute_plot(
data_df = "tbl_df",
attribute = "character"
)
Arguments
data_dfThe data to be analyzed.
(tibble::tibble)
attributeAttribute's name.
(character)
Returns
Composite result plot.
(gridExtra::grid.arrange)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.limitsOfQuantification$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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Description Format Details Active bindings Methods Author(s)
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
loqReturns the instance variable loq (tibble::tibble)
setLoqSets the instance variable loq. (tibble::tibble)
outliersReturns instance variable outliers (tibble::tibble)
lloqSubstituteAlphabetReturns the instance variable lloqSubstititeAlphabet (character)
lloqSubstituteAgentReturns the instance variable lloqSubstituteAgent (character)
setLloqSubstituteAgentSets the instance variable lloqSubstituteAgent. (character)
uloqSubstituteAlphabetReturns the instance variable uloqSubstititeAlphabet (character)
uloqSubstituteAgentReturns the instance variable uloqSubstituteAgent (character)
setUloqSubstituteAgentSets the instance variable uloqSubstituteAgent. (character)
naHandlingAlphabetReturns the instance variable naHandlingAlphabet (character)
naHandlingAgentReturns the instance variable naHandlingAgentt (character)
setNaHandlingAgentSets the instance variable naHandlingAgentt (character)
loqStatisticsReturns the instance variable loqStatistics
Methods
Public methods
Method new()
Mutates outlier candidates characterized as below LLOQ based on user defined actions.
Mutates outlier candidates characterized as above ULOQ based on user defined actions.
Searches for outliers in the given data frame. If an outlier was found, it is appended to the instance variable outliers. Indicates if an outlier was found.
Extends the instance variable outliers by one entry.
Tests if the provided attributes are known to the class.
Resets the class' instance variable outliers
Calculates statistics of outlier appearance. Stores it into the instance variable loqStatistics
Resets the class' instance variable loqStatistics
Resets the class by a data frame comprising information about LOQs.
Resets the class by a vector of attribute names. The Attributes' LOQs are initially assigned to na.
Clears the heap and
indicates that instance of pgu.limitaOfQuantification is removed from heap.
Creates and returns a new pgu.limitsOfQuantification object.
Usage
pgu.limitsOfQuantification$new(attribute_names = "character")
Arguments
attribute_namesVector of attribute names with to be analyzed by the loq object. (character)
Returns
A new pgu.limitsOfQuantification object.
(pguIMP::pgu.limitsOfQuantification)
Method print()
Prints instance variables of a pgu.limitsOfQuantification object.
Usage
pgu.limitsOfQuantification$print()
Returns
string
Method reset()
Resets the pguIMP::pgu.limitsOfQuantification object on the given parameters attribute_names and data_df
Usage
pgu.limitsOfQuantification$reset( attribute_names = "character", data_df = "tbl_df" )
Arguments
attribute_namesVector of attribute names with to be analyzed by the loq object. (character)
data_dfDataframe comprising loq information. Feature names need to be 'attribute', 'LLOQ' and 'ULOQ'. (tibble::tibble)
Method fit()
Analyses the data dets for instances outside of the LOQ defined value interval.
Usage
pgu.limitsOfQuantification$fit(data_df = "tbl_df")
Arguments
data_dfDataframe to be analyzed
Method predict()
Mutates all outlier candidates based on user defined actions.
Usage
pgu.limitsOfQuantification$predict(data_df = "tbl_df")
Arguments
data_dfThe data to be analyzed. (tibble::tibble)
Returns
The revised data frame (tibble::tibble)
Method attribute_lloq()
Returns the attribute's specific lloq.
Usage
pgu.limitsOfQuantification$attribute_lloq(attribute = "character")
Arguments
attributeThe attribute to be analyzed (character)
Returns
The attribute's lloq (numeric)
Method attribute_uloq()
Returns the attribute's specific uloq.
Usage
pgu.limitsOfQuantification$attribute_uloq(attribute = "character")
Arguments
attributeThe attribute to be analyzed (character)
Returns
The attribute's uloq (numeric)
Method set_attribute_lloq()
sets the attribute's specific lloq to value.
Usage
pgu.limitsOfQuantification$set_attribute_lloq( attribute = "character", value = NA )
Arguments
attributeThe attribute to be updated (character)
valueThe value parsed to the attributes lloq (numeric)
Method set_attribute_uloq()
sets the attribute's specific uloq to value.
Usage
pgu.limitsOfQuantification$set_attribute_uloq( attribute = "character", value = NA )
Arguments
attributeThe attribute to be updated (character)
valueThe value parsed to the attributes lloq (numeric)
Method attribute_outliers()
Returns the detected outliers of a given attribute.
Usage
pgu.limitsOfQuantification$attribute_outliers(attribute = "character")
Arguments
attributeThe attribute to be analyzed (character)
Returns
The attribute's outliers (tibble::tibble)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | ####################
# data information #
####################
#' @description
#' Gathers and returns class information
dataInformation = function(){
self$loq %>%
dplyr::summarise_all(class) %>%
tidyr::gather(variable, class) %>%
return()
}, #function
####################
# output functions #
####################
#' @description
#' Merges dfData and dfMetadata and returns a fromatted data table.
#' @param dfData
#' The data to be analyzed.
#' (tibble::tibble)
#' @param dfMetadata
#' The data frame containing metadata.
#' (tibble::tibble)
#' @return
#' A formatted data table
#' (DT::datatable)
loqDataTable = function(dfData = "tbl_df", dfMetadata = "tbl_df"){
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
t <- NULL
featureNames <- colnames(dfData)
tryCatch(
dfMerge <- dplyr::bind_cols(dfMetadata, dfData),
error = function(e){
print("error")
print(e)
dfMerge <- dfData
}#error
)#tryCatch
if(self$checkValidity(featureNames)){
t <- dfMerge %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
DT::datatable(options = list(scrollX = TRUE,
scrollY = '350px',
paging = FALSE))
for (featureName in featureNames){
featureOutlier <- self$outliers %>%
dplyr::filter(feature == featureName) %>%
dplyr::mutate_if(is.numeric, round, 3)
if (nrow(featureOutlier) > 0){
t <- DT::formatStyle(t,
featureName,
backgroundColor = DT::styleEqual(dfMerge %>%
dplyr::select(!!featureName) %>%
dplyr::slice(featureOutlier[["measurement"]]) %>%
unlist() %>%
round(digits = 3),
featureOutlier[["color"]]))
}#if
}#for
}#if
return(t)
}, #function
#' @description
#' Returns a formatted data table with comrising the information of a user defined attribute's outliers.
#' @param obj
#' The data to be analyzed.
#' (tibble::tibble)
#' @param feature
#' The attribute to be analyzed
#' (character)
#' @return
#' A formatted data table
#' (DT::datatable)
loqFeatureTable = function(obj = "tbl_df", feature = "character"){
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
t <- NULL
if(self$checkValidity(feature)){
featureOutlier <- self$outliers %>%
dplyr::filter(feature == !!feature) %>%
dplyr::mutate_if(is.numeric, round, 3)
dfFeature <- obj %>%
dplyr::mutate_if(is.numeric, round, 3)
print(dfFeature)
t <- dfFeature %>%
DT::datatable(options = list(scrollX = TRUE,
scrollY = '350px',
paging = FALSE))
if (nrow(featureOutlier) > 0){
t <- DT::formatStyle(
t,
feature,
backgroundColor = DT::styleEqual(dfFeature %>%
dplyr::select(!!feature) %>%
dplyr::slice(featureOutlier[["measurement"]]) %>%
unlist() %>%
round(digits = 3),
featureOutlier[["color"]]))
}#if
}#if
return(t)
}, #function
|
Method plot_loq_distribution()
Creates a plot of the instance variable loqStatistics.
Usage
pgu.limitsOfQuantification$plot_loq_distribution()
Returns
A plot. (ggplot2::ggplot)
Method attribute_bar_plot()
Creates a bar plot of a user defined attribute's value distribution. LOQs are indicated as dotted lines
Usage
pgu.limitsOfQuantification$attribute_bar_plot( data_df = "tbl_df", attribute = "character" )
Arguments
data_dfThe data to be analyzed. (tibble::tibble)
attributeThe attribute to be analyzed (character)
Returns
A bar plot. (ggplot2::ggplot)
Method attribute_box_plot_with_subset()
Creates a box plot of a user defined attribute's value distribution. LOQs are indicated as dotted lines
Usage
pgu.limitsOfQuantification$attribute_box_plot_with_subset( data_df = "tbl_df", attribute = "character" )
Arguments
data_dfThe data to be analyzed. (tibble::tibble)
attributeThe attribute to be analyzed (character)
Returns
A box plot. (ggplot2::ggplot)
Method attribute_plot()
Creates and returns a composite graphical analysis of the outlier analysis of a user defined attribute.
Usage
pgu.limitsOfQuantification$attribute_plot( data_df = "tbl_df", attribute = "character" )
Arguments
data_dfThe data to be analyzed. (tibble::tibble)
attributeAttribute's name. (character)
Returns
Composite result plot. (gridExtra::grid.arrange)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.limitsOfQuantification$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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