R/pguCorrelator.R
In SMLMS/pguIMP: 'pguIMP'
#' @title pgu.correlator
#'
#' @description
#' An R6 class that performs pairwise correlation on the pguIMP data set.
#'
#' @format [R6::R6Class] object.
#' @section Construction:
#' x <- pguIMP::pgu.correlator$new()
#'
#' @importFrom R6 R6Class
#' @importFrom magrittr %>%
#' @importFrom dplyr select select_if pull rename mutate everything
#' @importFrom tibble tibble as_tibble is_tibble rownames_to_column
#'
#' @importFrom stats cor.test
#' @examples
#' require(dplyr)
#' require(tibble)
#' data(iris)
#' data_df <- iris %>%
#' tibble::as_tibble() %>%
#' dplyr::select(-c("Species"))
#' correlator = pguIMP::pgu.correlator$new(data_df)
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#' @export
#'
pgu.correlator <- R6::R6Class("pgu.correlator",
####################
# instance variables
####################
private = list(
.featureNames = "character",
.method = "character",
.r = "matrix",
.pPearson = "matrix",
.tau = "matrix",
.pKendall = "matrix",
.rho = "matrix",
.pSpearman = "matrix",
.abscissa = "character",
.ordinate = "character",
.test = "htest"
),
##################
# accessor methods
##################
active = list(
#' @field featureNames
#' Returns the instance variable featureNames.
#' (character)
featureNames = function(){
return(private$.featureNames)
},
#' @field setFeatureNames
#' Sets the instance variable featureNames.
#' It further initializes the instance variables:
#' intercept, pIntercept, slope, pSlope.
#' (character)
setFeatureNames = function(names = "character"){
private$.featureNames <- names
private$.r <- self$resetMatrix(value = 0)
private$.pPearson <- self$resetMatrix(value = 1)
private$.tau <- self$resetMatrix(value = 0)
private$.pKendall <- self$resetMatrix(value = 1)
private$.rho <- self$resetMatrix(value = 0)
private$.pSpearman <- self$resetMatrix(value = 1)
},
#' @field method
#' Returns the instance variable method.
#' (character)
method = function(){
return(private$.method)
},
#' @field r
#' Returns the instance variable r.
#' (matrix)
r = function(){
return(private$.r)
},
#' @field pPearson
#' Returns the instance variable pPearson.
#' (matrix)
pPearson = function(){
return(private$.pPearson)
},
#' @field tau
#' Returns the instance variable tau.
#' (matrix)
tau = function(){
return(private$.tau)
},
#' @field pKendall
#' Returns the instance variable pKendall.
#' (matrix)
pKendall = function(){
return(private$.pKendall)
},
#' @field rho
#' Returns the instance variable rho.
#' (matrix)
rho = function(){
return(private$.rho)
},
#' @field pSpearman
#' Returns the instance variable pSpearman.
#' (matrix)
pSpearman = function(){
return(private$.pSpearman)
},
#' @field abscissa
#' Returns the instance variable abscissa.
#' (character)
abscissa = function(){
return(private$.abscissa)
},
#' @field setAbscissa
#' Sets the instance variable abscicca to value.
setAbscissa = function(value = "character"){
private$.abscissa <- value
},
#' @field ordinate
#' Returns the instance variable ordinate.
#' (character)
ordinate = function(){
return(private$.ordinate)
},
#' @field setOrdinate
#' Sets the instance variable ordinate to value.
setOrdinate = function(value = "character"){
private$.ordinate <- value
},
#' @field test
#' Returns the instance variable test.
#' (stats::cor.test)
test = function(){
return(private$.test)
}
),
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.correlator` object.
#' @param data
#' The data to be modeled.
#' (tibble::tibble)
#' @return
#' A new `pgu.correlator` object.
#' (pguIMP::pgu.correlator)
initialize = function(data = "tbl_df"){
if(!tibble::is_tibble(data)){
data <- tibble::tibble(names <- "none",
values <- c(NA))
}
self$resetCorrelator(data)
},
#' @description
#' Clears the heap and
#' indicates if instance of `pgu.correlator` is removed from heap.
finalize = function(){
print("Instance of pgu.correlator removed from heap")
},
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.correlator` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.correlator\n")
cat(rString)
print(self$featureNames)
mString <- sprintf("\nmethod: %s\n", self$method[1])
cat(mString)
coefString <- sprintf("\nPearson's r:\n")
cat(coefString)
print(self$r)
pString <- sprintf("\nPearson's p-Value:\n")
cat(pString)
print(self$pPearson)
mString <- sprintf("\nmethod: %s\n", self$method[2])
cat(mString)
coefString <- sprintf("\nKendall's tau:\n")
cat(coefString)
print(self$tau)
pString <- sprintf("\nKendall's p-Value:\n")
cat(pString)
print(self$pKendall)
mString <- sprintf("\nmethod: %s\n", self$method[3])
cat(mString)
coefString <- sprintf("\nSpearman's rho:\n")
cat(coefString)
print(self$rho)
pString <- sprintf("\nSpearman's p-Value:\n")
cat(pString)
print(self$pPearson)
cat("\n\n")
invisible(self)
},#function
####################
# public functions #
####################
#' @description
#' Performes pair-wise correlation analysis on the attributes of the data frame.
#' Progresse is indicated by the progress object passed to the function.
#' @param data
#' Dataframe with at least two numeric attributes.
#' (tibble::tibble)
#' @param progress
#' Keeps track of the analysis progress.
#' (shiny::Progress)
resetCorrelator = function(data = "tbl_df", progress = "Progress"){
private$.featureNames <- data %>%
dplyr::select_if(is.numeric) %>%
colnames()
private$.method <- c("pearson", "kendall","spearman")
private$.r <- self$resetMatrix(value = 0)
private$.pPearson <- self$resetMatrix(value = 1)
private$.tau <- self$resetMatrix(value = 0)
private$.pKendall <- self$resetMatrix(value = 1)
private$.rho <- self$resetMatrix(value = 0)
private$.pSpearman <- self$resetMatrix(value = 1)
self$correlate(data, progress)
},#function
#' @description
#' Creates a square matrix which dimension corresponds to the length
#' of the instance variable featureNames. The matrix entries are set to a distict `value`.
#' @param value
#' The value the matrix entries are set to.
#' (numeric)
#' @return
#' A square matrix.
#' (matrix)
resetMatrix = function(value = "numeric"){
n = length(self$featureNames)
df <- matrix(data = value,
nrow = n,
ncol = n,
dimnames = list(self$featureNames, self$featureNames))
return(df)
},#function
####################
# helper functions #
####################
#' @description
#' Determines the numerical index of the column of an attribute based on the attribute name.
#' @param feature
#' The attribute's name.
#' (character)
#' @return
#' The attributes column index.
#' (numeric)
featureIdx = function(feature = "character"){
idx <- match(feature, self$featureNames)
if(is.na(idx)){
rString <- sprintf("\nWarning in pgu.correlator: feature %s is not known\n",
feature)
cat(rString)
}
return(idx)
},#function
#########################
# correlation functions #
#########################
#' @description
#' Creates a correlation test between two attributes of a dataframe.
#' The test is stored as instance variable.
#' @param abscissa
#' The abscissa values.
#' (numeric)
#' @param ordinate
#' The ordinate values.
#' (numeric)
#' @param method
#' The cname of the correlation test.
#' Valid coiced are defined by the instance variable `method`.
#' (chatacter)
calcCorrelationNumeric = function(abscissa = "numeric", ordinate = "numeric", method = "character"){
private$.test <- stats::cor.test(x = abscissa,
y = ordinate,
alternative = "two.sided",
exact = FALSE,
method = method)
},#function
#' @description
#' Performs the actual correlation test routine after Pearson.
#' Iteratively runs through the attributes known to the class
#' and calculates Pearson's correlation for each valid attribute pair.
#' The test results are stored in the instance variables:
#' r, pPearson.
#' Here, pX represents the p-value of the respective parameter X.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
createCorrelationMatrixPearson = function(data = "tbl_df", progress = "Progress"){
for (abs in self$featureNames){
for (ord in self$featureNames){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
self$calcCorrelationNumeric(abscissa = data %>%
dplyr::pull(abs),
ordinate = data %>%
dplyr::pull(ord),
method = self$method[1])
private$.r[ord,abs] <-self$test$estimate[[1]]
private$.pPearson[ord, abs] <- self$test$p.value
}#for
}#for
},#function
#' @description
#' Performs the actual correlation test routine after Kendall.
#' Iteratively runs through the attributes known to the class
#' and calculates Kendall's correlation for each valid attribute pair.
#' The test results are stored in the instance variables:
#' tau, pKendall.
#' Here, pX represents the p-value of the respective parameter X.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
createCorrelationMatrixKendall = function(data = "tbl_df", progress = "Progress"){
for (abs in self$featureNames){
for (ord in self$featureNames){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
self$calcCorrelationNumeric(abscissa = data %>%
dplyr::pull(abs),
ordinate = data %>%
dplyr::pull(ord),
method = self$method[2])
private$.tau[ord,abs] <-self$test$estimate[[1]]
private$.pKendall[ord, abs] <- self$test$p.value
}#for
}#for
},#function
#' @description
#' Performs the actual correlation test routine after Spearman.
#' Iteratively runs through the attributes known to the class
#' and calculates Spearman's correlation for each valid attribute pair.
#' The test results are stored in the instance variables:
#' rho, pSpearman.
#' Here, pX represents the p-value of the respective parameter X.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
createCorrelationMatrixSpearman = function(data = "tbl_df", progress = "Progress"){
for (abs in self$featureNames){
for (ord in self$featureNames){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
self$calcCorrelationNumeric(abscissa = data %>%
dplyr::pull(abs),
ordinate = data %>%
dplyr::pull(ord),
method = self$method[3])
private$.rho[ord,abs] <-self$test$estimate[[1]]
private$.pSpearman[ord, abs] <- self$test$p.value
}#for
}#for
},#function
#' @description
#' Performs the all three correlation test routines defined
#' within the instance variable `method`.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
correlate = function(data = "tbl_df", progress = "Progress"){
self$createCorrelationMatrixPearson(data, progress)
self$createCorrelationMatrixKendall(data, progress)
self$createCorrelationMatrixSpearman(data, progress)
},#function
###################
# print functions #
###################
#' @description
#' Transforms the results of the correlation procedure for a valid pair of attributes to a dataframe
#' and returns it.
#' @return
#' The analyis result as a dataframe.
#' (tibble::tibble)
printFeature = function(){
df <- data.frame(
abscissa = self$abscissa,
ordinate = self$ordinate,
r = self$r[self$ordinate, self$abscissa],
p.Pearson = self$pPearson[self$ordinate, self$abscissa],
tau = self$tau[self$ordinate, self$abscissa],
p.Kendall = self$pPearson[self$ordinate, self$abscissa],
rho = self$rho[self$ordinate, self$abscissa],
p.Spearman = self$pSpearman[self$ordinate, self$abscissa]) %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column("correlation parameter") %>%
tibble::as_tibble() %>%
dplyr::rename(value = "V1")
return(df)
},#function
#' @description
#' Transfroms instance variable `r` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `r`.
#' (tibble::tibble)
printRTbl = function(){
self$r %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `pPearson` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `pPearson`.
#' (tibble::tibble)
printPPearsonTbl = function(){
self$pPearson %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `tau` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `tau`.
#' (tibble::tibble)
printTauTbl = function(){
self$tau %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `pKendall` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `pKendall`.
#' (tibble::tibble)
printPKendallTbl = function(){
self$pKendall %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `rho` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `rho`.
#' (tibble::tibble)
printRhoTbl = function(){
self$rho %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `pSpearman` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `pSpearman`.
#' (tibble::tibble)
printPSpearmanTbl = function(){
self$pSpearman %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
}#function
)#public
)#Class
SMLMS/pguIMP documentation built on Oct. 1, 2021, 12:01 a.m.
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#' @title pgu.correlator
#'
#' @description
#' An R6 class that performs pairwise correlation on the pguIMP data set.
#'
#' @format [R6::R6Class] object.
#' @section Construction:
#' x <- pguIMP::pgu.correlator$new()
#'
#' @importFrom R6 R6Class
#' @importFrom magrittr %>%
#' @importFrom dplyr select select_if pull rename mutate everything
#' @importFrom tibble tibble as_tibble is_tibble rownames_to_column
#'
#' @importFrom stats cor.test
#' @examples
#' require(dplyr)
#' require(tibble)
#' data(iris)
#' data_df <- iris %>%
#' tibble::as_tibble() %>%
#' dplyr::select(-c("Species"))
#' correlator = pguIMP::pgu.correlator$new(data_df)
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#' @export
#'
pgu.correlator <- R6::R6Class("pgu.correlator",
####################
# instance variables
####################
private = list(
.featureNames = "character",
.method = "character",
.r = "matrix",
.pPearson = "matrix",
.tau = "matrix",
.pKendall = "matrix",
.rho = "matrix",
.pSpearman = "matrix",
.abscissa = "character",
.ordinate = "character",
.test = "htest"
),
##################
# accessor methods
##################
active = list(
#' @field featureNames
#' Returns the instance variable featureNames.
#' (character)
featureNames = function(){
return(private$.featureNames)
},
#' @field setFeatureNames
#' Sets the instance variable featureNames.
#' It further initializes the instance variables:
#' intercept, pIntercept, slope, pSlope.
#' (character)
setFeatureNames = function(names = "character"){
private$.featureNames <- names
private$.r <- self$resetMatrix(value = 0)
private$.pPearson <- self$resetMatrix(value = 1)
private$.tau <- self$resetMatrix(value = 0)
private$.pKendall <- self$resetMatrix(value = 1)
private$.rho <- self$resetMatrix(value = 0)
private$.pSpearman <- self$resetMatrix(value = 1)
},
#' @field method
#' Returns the instance variable method.
#' (character)
method = function(){
return(private$.method)
},
#' @field r
#' Returns the instance variable r.
#' (matrix)
r = function(){
return(private$.r)
},
#' @field pPearson
#' Returns the instance variable pPearson.
#' (matrix)
pPearson = function(){
return(private$.pPearson)
},
#' @field tau
#' Returns the instance variable tau.
#' (matrix)
tau = function(){
return(private$.tau)
},
#' @field pKendall
#' Returns the instance variable pKendall.
#' (matrix)
pKendall = function(){
return(private$.pKendall)
},
#' @field rho
#' Returns the instance variable rho.
#' (matrix)
rho = function(){
return(private$.rho)
},
#' @field pSpearman
#' Returns the instance variable pSpearman.
#' (matrix)
pSpearman = function(){
return(private$.pSpearman)
},
#' @field abscissa
#' Returns the instance variable abscissa.
#' (character)
abscissa = function(){
return(private$.abscissa)
},
#' @field setAbscissa
#' Sets the instance variable abscicca to value.
setAbscissa = function(value = "character"){
private$.abscissa <- value
},
#' @field ordinate
#' Returns the instance variable ordinate.
#' (character)
ordinate = function(){
return(private$.ordinate)
},
#' @field setOrdinate
#' Sets the instance variable ordinate to value.
setOrdinate = function(value = "character"){
private$.ordinate <- value
},
#' @field test
#' Returns the instance variable test.
#' (stats::cor.test)
test = function(){
return(private$.test)
}
),
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.correlator` object.
#' @param data
#' The data to be modeled.
#' (tibble::tibble)
#' @return
#' A new `pgu.correlator` object.
#' (pguIMP::pgu.correlator)
initialize = function(data = "tbl_df"){
if(!tibble::is_tibble(data)){
data <- tibble::tibble(names <- "none",
values <- c(NA))
}
self$resetCorrelator(data)
},
#' @description
#' Clears the heap and
#' indicates if instance of `pgu.correlator` is removed from heap.
finalize = function(){
print("Instance of pgu.correlator removed from heap")
},
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.correlator` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.correlator\n")
cat(rString)
print(self$featureNames)
mString <- sprintf("\nmethod: %s\n", self$method[1])
cat(mString)
coefString <- sprintf("\nPearson's r:\n")
cat(coefString)
print(self$r)
pString <- sprintf("\nPearson's p-Value:\n")
cat(pString)
print(self$pPearson)
mString <- sprintf("\nmethod: %s\n", self$method[2])
cat(mString)
coefString <- sprintf("\nKendall's tau:\n")
cat(coefString)
print(self$tau)
pString <- sprintf("\nKendall's p-Value:\n")
cat(pString)
print(self$pKendall)
mString <- sprintf("\nmethod: %s\n", self$method[3])
cat(mString)
coefString <- sprintf("\nSpearman's rho:\n")
cat(coefString)
print(self$rho)
pString <- sprintf("\nSpearman's p-Value:\n")
cat(pString)
print(self$pPearson)
cat("\n\n")
invisible(self)
},#function
####################
# public functions #
####################
#' @description
#' Performes pair-wise correlation analysis on the attributes of the data frame.
#' Progresse is indicated by the progress object passed to the function.
#' @param data
#' Dataframe with at least two numeric attributes.
#' (tibble::tibble)
#' @param progress
#' Keeps track of the analysis progress.
#' (shiny::Progress)
resetCorrelator = function(data = "tbl_df", progress = "Progress"){
private$.featureNames <- data %>%
dplyr::select_if(is.numeric) %>%
colnames()
private$.method <- c("pearson", "kendall","spearman")
private$.r <- self$resetMatrix(value = 0)
private$.pPearson <- self$resetMatrix(value = 1)
private$.tau <- self$resetMatrix(value = 0)
private$.pKendall <- self$resetMatrix(value = 1)
private$.rho <- self$resetMatrix(value = 0)
private$.pSpearman <- self$resetMatrix(value = 1)
self$correlate(data, progress)
},#function
#' @description
#' Creates a square matrix which dimension corresponds to the length
#' of the instance variable featureNames. The matrix entries are set to a distict `value`.
#' @param value
#' The value the matrix entries are set to.
#' (numeric)
#' @return
#' A square matrix.
#' (matrix)
resetMatrix = function(value = "numeric"){
n = length(self$featureNames)
df <- matrix(data = value,
nrow = n,
ncol = n,
dimnames = list(self$featureNames, self$featureNames))
return(df)
},#function
####################
# helper functions #
####################
#' @description
#' Determines the numerical index of the column of an attribute based on the attribute name.
#' @param feature
#' The attribute's name.
#' (character)
#' @return
#' The attributes column index.
#' (numeric)
featureIdx = function(feature = "character"){
idx <- match(feature, self$featureNames)
if(is.na(idx)){
rString <- sprintf("\nWarning in pgu.correlator: feature %s is not known\n",
feature)
cat(rString)
}
return(idx)
},#function
#########################
# correlation functions #
#########################
#' @description
#' Creates a correlation test between two attributes of a dataframe.
#' The test is stored as instance variable.
#' @param abscissa
#' The abscissa values.
#' (numeric)
#' @param ordinate
#' The ordinate values.
#' (numeric)
#' @param method
#' The cname of the correlation test.
#' Valid coiced are defined by the instance variable `method`.
#' (chatacter)
calcCorrelationNumeric = function(abscissa = "numeric", ordinate = "numeric", method = "character"){
private$.test <- stats::cor.test(x = abscissa,
y = ordinate,
alternative = "two.sided",
exact = FALSE,
method = method)
},#function
#' @description
#' Performs the actual correlation test routine after Pearson.
#' Iteratively runs through the attributes known to the class
#' and calculates Pearson's correlation for each valid attribute pair.
#' The test results are stored in the instance variables:
#' r, pPearson.
#' Here, pX represents the p-value of the respective parameter X.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
createCorrelationMatrixPearson = function(data = "tbl_df", progress = "Progress"){
for (abs in self$featureNames){
for (ord in self$featureNames){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
self$calcCorrelationNumeric(abscissa = data %>%
dplyr::pull(abs),
ordinate = data %>%
dplyr::pull(ord),
method = self$method[1])
private$.r[ord,abs] <-self$test$estimate[[1]]
private$.pPearson[ord, abs] <- self$test$p.value
}#for
}#for
},#function
#' @description
#' Performs the actual correlation test routine after Kendall.
#' Iteratively runs through the attributes known to the class
#' and calculates Kendall's correlation for each valid attribute pair.
#' The test results are stored in the instance variables:
#' tau, pKendall.
#' Here, pX represents the p-value of the respective parameter X.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
createCorrelationMatrixKendall = function(data = "tbl_df", progress = "Progress"){
for (abs in self$featureNames){
for (ord in self$featureNames){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
self$calcCorrelationNumeric(abscissa = data %>%
dplyr::pull(abs),
ordinate = data %>%
dplyr::pull(ord),
method = self$method[2])
private$.tau[ord,abs] <-self$test$estimate[[1]]
private$.pKendall[ord, abs] <- self$test$p.value
}#for
}#for
},#function
#' @description
#' Performs the actual correlation test routine after Spearman.
#' Iteratively runs through the attributes known to the class
#' and calculates Spearman's correlation for each valid attribute pair.
#' The test results are stored in the instance variables:
#' rho, pSpearman.
#' Here, pX represents the p-value of the respective parameter X.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
createCorrelationMatrixSpearman = function(data = "tbl_df", progress = "Progress"){
for (abs in self$featureNames){
for (ord in self$featureNames){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
self$calcCorrelationNumeric(abscissa = data %>%
dplyr::pull(abs),
ordinate = data %>%
dplyr::pull(ord),
method = self$method[3])
private$.rho[ord,abs] <-self$test$estimate[[1]]
private$.pSpearman[ord, abs] <- self$test$p.value
}#for
}#for
},#function
#' @description
#' Performs the all three correlation test routines defined
#' within the instance variable `method`.
#' Displays the progress if shiny is loaded.
#' @param data
#' The data to be analysed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored within this instance of the shiny Progress class.
#' (shiny::Progress)
correlate = function(data = "tbl_df", progress = "Progress"){
self$createCorrelationMatrixPearson(data, progress)
self$createCorrelationMatrixKendall(data, progress)
self$createCorrelationMatrixSpearman(data, progress)
},#function
###################
# print functions #
###################
#' @description
#' Transforms the results of the correlation procedure for a valid pair of attributes to a dataframe
#' and returns it.
#' @return
#' The analyis result as a dataframe.
#' (tibble::tibble)
printFeature = function(){
df <- data.frame(
abscissa = self$abscissa,
ordinate = self$ordinate,
r = self$r[self$ordinate, self$abscissa],
p.Pearson = self$pPearson[self$ordinate, self$abscissa],
tau = self$tau[self$ordinate, self$abscissa],
p.Kendall = self$pPearson[self$ordinate, self$abscissa],
rho = self$rho[self$ordinate, self$abscissa],
p.Spearman = self$pSpearman[self$ordinate, self$abscissa]) %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column("correlation parameter") %>%
tibble::as_tibble() %>%
dplyr::rename(value = "V1")
return(df)
},#function
#' @description
#' Transfroms instance variable `r` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `r`.
#' (tibble::tibble)
printRTbl = function(){
self$r %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `pPearson` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `pPearson`.
#' (tibble::tibble)
printPPearsonTbl = function(){
self$pPearson %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `tau` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `tau`.
#' (tibble::tibble)
printTauTbl = function(){
self$tau %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `pKendall` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `pKendall`.
#' (tibble::tibble)
printPKendallTbl = function(){
self$pKendall %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `rho` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `rho`.
#' (tibble::tibble)
printRhoTbl = function(){
self$rho %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
},#function
#' @description
#' Transfroms instance variable `pSpearman` to a dataframe and returns it.
#' @return
#' Dataframe of instance variable `pSpearman`.
#' (tibble::tibble)
printPSpearmanTbl = function(){
self$pSpearman %>%
tibble::as_tibble() %>%
dplyr::mutate(features = self$featureNames) %>%
dplyr::select(features, dplyr::everything()) %>%
return()
}#function
)#public
)#Class
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