Package 'ClustAll' reference manual

Title:	ClustAll: Data driven strategy to find groups of patients within complex diseases
Description:	Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types).
Authors:	Asier Ortega-Legarreta [aut, cre] , Sara Palomino-Echeverria [aut]
Maintainer:	Asier Ortega-Legarreta <[email protected]>
License:	GPL-2
Version:	1.1.0
Built:	2024-07-17 11:36:03 UTC
Source:	https://github.com/bioc/ClustAll

Add the validation data into the ClustAllObject

Description

Generic function to add validation data to the ClustAllObject-class object

Usage

addValidationData(Object, dataValidation)
addValidationData(Object, dataValidation)

Arguments

`Object`	`ClustAllObject-class` object
`dataValidation`	numericOrCharacter

Details

addValidationData

Value

ClustAllObject-class object

Examples

data("BreastCancerWisconsin", package = "ClustAll")
label <- as.numeric(as.factor(wdbc$Diagnosis))
wdbc <- wdbc[,-c(1, 2)] # delete patients IDs & label
obj_noNA <- createClustAll(data = wdbc)
obj_noNA <- addValidationData(Object = obj_noNA,
                              dataValidation = label)
data("BreastCancerWisconsin", package = "ClustAll")
label <- as.numeric(as.factor(wdbc$Diagnosis))
wdbc <- wdbc[,-c(1, 2)] # delete patients IDs & label
obj_noNA <- createClustAll(data = wdbc)
obj_noNA <- addValidationData(Object = obj_noNA,
                              dataValidation = label)

characterOrNA Class union of character, null or missing

Description

Contains either character, NULL or missing object

Value

characterOrNA class object

ClustAllObject

Description

Stores the original data used, the imputed datasets and the results of the ClustAll pipeline.

Value

ClustAllObject class object

Slots

data: Data Frame of the data used. Maybe modified from the input data.
dataOriginal: Data Frame of the original data introduced.
dataImputed: Mids object derived from the mice package that stores the imputed data, in case imputation was applied. Otherwise NULL.
dataValidation: labelling numericOrNA. Original data labelling.
nImputation: Number of multiple imputations to be applied.
processed: Logical if the ClustAll pipeline has been executed previously
summary_clusters: listOrNULL. List with the resulting stratifications for each combination of clustering methods (distance + clustering algorithm) and depth, in case ClustAll pipeline has been executed previously. Otherwise NULL.
JACCARD_DISTANCE_F: matrixOrNULL. Matrix containing the Jaccard distances derived from the robust populations stratifications if ClustAll pipeline has been executed previously. Otherwise NULL.

cluster2data

Description

Returns the original data in a dataframe, including the selected robust stratification(s) as varaibles. The representative stratification names can be obtained using the method. resStratification

Usage

cluster2data(Object,
                    stratificationName)
cluster2data(Object,
                    stratificationName)

Arguments

`Object`	`ClustAllObject-class` object
`stratificationName`	Character vector with one or more stratification names

Value

data.frame

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)
df <- cluster2data(Object = obj_noNA1,
                   stratificationName = c("cuts_a_1","cuts_b_5","cuts_a_5"))

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)
df <- cluster2data(Object = obj_noNA1,
                   stratificationName = c("cuts_a_1","cuts_b_5","cuts_a_5"))

Creates ClustAllObject and perform imputations to deal with missing values

Description

This pipeline creates the ClustAllObject and computes the imputations if the dataset contains missing values. The next step would be runClustAll

Usage

createClustAll(data=data,
                      nImputation=NULL,
                      dataImputed=NULL,
                      colValidation=NULL)
createClustAll(data=data,
                      nImputation=NULL,
                      dataImputed=NULL,
                      colValidation=NULL)

Arguments

`data`	Data Frame of the using data. It may contain missing (NA) values.
`nImputation`	Numeric value with the number of imputations to be computed in case the data contains NAs.
`dataImputed`	mids object created with mice package. The introduced data for the imputation and the data using must be the same.
`colValidation`	Character value with the original labelling of the input data.

Value

An object of class ClustAllObject-class

Examples

# Scenario 1: data does not contain missing values
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- wdbc[,-c(1,2)]
obj_noNA <- createClustAll(data = wdbc)

# Scenario 2: data contains NAs and imputed data is provided automatically
data("BreastCancerWisconsinMISSING", package = "ClustAll") # load example data
obj_NA <- createClustAll(wdbcNA, nImputation = 5)

# Scenario 3: data contains NAs and imputed data is provided manually
data("BreastCancerWisconsinMISSING", package = "ClustAll") # load the example data
ini <- mice::mice(wdbcNA, maxit = 0, print = FALSE)
pred <- ini$pred # predictor matrix
pred["radius1", c("perimeter1", "area1", "smoothness1")] <- 0 # example of how to remove predictors
imp <- mice::mice(wdbcNA, m=5, pred=pred, maxit=5, seed=1234, print=FALSE)
obj_imp <- createClustAll(data=wdbcNA, dataImputed = imp)

# Scenario 1: data does not contain missing values
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- wdbc[,-c(1,2)]
obj_noNA <- createClustAll(data = wdbc)

# Scenario 2: data contains NAs and imputed data is provided automatically
data("BreastCancerWisconsinMISSING", package = "ClustAll") # load example data
obj_NA <- createClustAll(wdbcNA, nImputation = 5)

# Scenario 3: data contains NAs and imputed data is provided manually
data("BreastCancerWisconsinMISSING", package = "ClustAll") # load the example data
ini <- mice::mice(wdbcNA, maxit = 0, print = FALSE)
pred <- ini$pred # predictor matrix
pred["radius1", c("perimeter1", "area1", "smoothness1")] <- 0 # example of how to remove predictors
imp <- mice::mice(wdbcNA, m=5, pred=pred, maxit=5, seed=1234, print=FALSE)
obj_imp <- createClustAll(data=wdbcNA, dataImputed = imp)

Retrieve the imputed data from ClustAllObject

Description

Generic function to retrieve the imputed data obtained in createClustAll from a ClustAllObject-class object

Usage

dataImputed(Object)
dataImputed(Object)

Arguments

Object

ClustAllObject-class object

Value

Mids class object with the imputed data or NULL if imputation was not required

Examples

data("BreastCancerWisconsinMISSING", package = "ClustAll")
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_NA <- createClustAll(data = wdbcNA, colValidation = "Diagnosis",
                         dataImputed = wdbcMIDS)
dataImputed(obj_NA)
data("BreastCancerWisconsinMISSING", package = "ClustAll")
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_NA <- createClustAll(data = wdbcNA, colValidation = "Diagnosis",
                         dataImputed = wdbcMIDS)
dataImputed(obj_NA)

Retrieve the initial dataOriginal from ClustAllObject

Description

Generic function to retrieve the initial data used for createClustAll from a ClustAllObject-class object

Usage

dataOriginal(Object)
dataOriginal(Object)

Arguments

Object

ClustAllObject-class object

Value

The Data Frame with the initial data

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_noNA <- createClustAll(data = wdbc, colValidation = "Diagnosis")
dataOriginal(obj_noNA)
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_noNA <- createClustAll(data = wdbc, colValidation = "Diagnosis")
dataOriginal(obj_noNA)

Retrieve the original data labelling from ClustAllObject

Description

Generic function to retrieve numeric vector if it has been added with the true labels from a ClustAllObject-class object

Usage

dataValidation(Object)
dataValidation(Object)

Arguments

Object

ClustAllObject-class object

Value

numeric vector if true labels have been added. Otherwise NULL

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_noNA <- createClustAll(data = wdbc, colValidation="Diagnosis")
dataValidation(obj_noNA)
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_noNA <- createClustAll(data = wdbc, colValidation="Diagnosis")
dataValidation(obj_noNA)

initializeClustAllObject

Description

constuctor for ClustAllObject-class

Usage

## S4 method for signature 'ClustAllObject'
initialize(
  .Object,
  data,
  dataOriginal,
  dataImputed,
  dataValidation,
  nImputation,
  processed,
  summary_clusters,
  JACCARD_DISTANCE_F
)
## S4 method for signature 'ClustAllObject'
initialize(
  .Object,
  data,
  dataOriginal,
  dataImputed,
  dataValidation,
  nImputation,
  processed,
  summary_clusters,
  JACCARD_DISTANCE_F
)

Arguments

`.Object`	initializing object
`data`	Data Frame of the data used. Maybe modified from the input data.
`dataOriginal`	Data Frame of the original data introduced.
`dataImputed`	Mids object derived from the mice package that stores the imputed data, in case imputation was applied. Otherwise NULL.
`dataValidation`	labelling numericOrNA. Original data labelling.
`nImputation`	Number of multiple imputations to be applied.
`processed`	Logical if the ClustAll pipeline has been executed previously
`summary_clusters`	listOrNULL. List with the resulting stratifications for each combination of clustering methods (distance + clustering algorithm) and depth, in case ClustAll pipeline has been executed previously. Otherwise NULL.
`JACCARD_DISTANCE_F`	matrixOrNULL. Matrix containing the Jaccard distances derived from the robust populations stratifications if ClustAll pipeline has been executed previously. Otherwise NULL.

Value

An object of class ClustAllObject-class

Retrieve the matrix with the Jaccard distances derived from the robust populations stratifications in ClustAllObject

Description

Generic function to retrieve the matrix with the Jaccard distances derived from the robust populations stratifications inrunClustAll from a ClustAllObject-class object

Usage

JACCARD_DISTANCE_F(Object)
JACCARD_DISTANCE_F(Object)

Arguments

Object

ClustAllObject-class object

Value

Matrix containing the Jaccard distances derived from the robust populations stratifications or NULL if runClustAll method has not been executed yet

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = FALSE)
JACCARD_DISTANCE_F(obj_noNA1)
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = FALSE)
JACCARD_DISTANCE_F(obj_noNA1)

Class Union listOrNULL

Description

Contains either list, NULL or missing object

Details

Class union of list, null or missing

Value

listOrNULL class object

logicalOrNA

Description

Contains either logical, NULL or missing object

Details

Class union of logical, null or missing

Value

logicalOrNA class object

matrixOrNULL

Description

Contains either matrix or NULL object

Details

Class union of matrix, null or missing

Value

matrixOrNULL class object

Retrieve the number of imputations applied at the imputation step from ClustAllObject

Description

Generic function to retrieve the number of imputations in createClustAll from a ClustAllObject-class object

Usage

nImputation(Object)
nImputation(Object)

Arguments

Object

ClustAllObject-class object

Value

Numeric vector that contains the number of imputations. 0 in the case of no imputations were required

Examples

data("BreastCancerWisconsinMISSING", package = "ClustAll")
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_NA <- createClustAll(data = wdbcNA, colValidation = "Diagnosis",
                         dataImputed = wdbcMIDS)
nImputation(obj_NA)
data("BreastCancerWisconsinMISSING", package = "ClustAll")
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_NA <- createClustAll(data = wdbcNA, colValidation = "Diagnosis",
                         dataImputed = wdbcMIDS)
nImputation(obj_NA)

numericOrCharacter

Description

Contains either numeric or character object

Details

Class union of numericor character

Value

numericOrCharacter class object

Class Union numericOrNA

Description

Contains either numeric, NULL or missing object

Details

Class union of numeric, null or missing

Value

numericOrNA class object

obj_noNA1: Processed wdbc dataset for testing purposed

Description

Processed wdbc as appear in vignette

Usage

data("testData", package = "ClustAll")
data("testData", package = "ClustAll")

Format

A processed ClustAllObject

Value

ClustAllObject Object

obj_noNA1simplify: Processed wdbc dataset for testing purposed

Description

Processed wdbc as appear in vignette, with simplify TRUE parameter

Usage

data("testData", package = "ClustAll")
data("testData", package = "ClustAll")

Format

A processed ClustAllObject

Value

ClustAllObject Object

obj_noNAno1Validation: Processed wdbc dataset for testing purposed

Description

Processed wdbc as appear in vignette, with no validation data

Usage

data("testData", package = "ClustAll")
data("testData", package = "ClustAll")

Format

A processed ClustAllObject

Value

ClustAllObject Object

Correlation matrix heatmap showing the Jaccard distance between robust stratifications in the ClustAllObject

Description

This function plots the correlation matrix heatmap showing the Jaccard Distance between robust stratifications

Usage

plotJACCARD(Object,
                   paint=TRUE,
                   stratification_similarity=0.7)
plotJACCARD(Object,
                   paint=TRUE,
                   stratification_similarity=0.7)

Arguments

`Object`	`ClustAllObject-class` object
`paint`	Logical vector with the annotation for the different stratifications
`stratification_similarity`	The minimum Jaccard Distance value to consider two stratifications similar. Default is 0.7.

Value

plot

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
plotJACCARD(obj_noNA1, paint = TRUE, stratification_similarity = 0.9)

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
plotJACCARD(obj_noNA1, paint = TRUE, stratification_similarity = 0.9)

Plots Sankey Diagram showing the cluster distribution and shifts between a pair of stratifications derived from ClustAllObject

Description

This function plots the Sankey Diagram with the cluster distribution and shifts between a pair of stratifications

Usage

plotSANKEY(Object,
                  clusters,
                  validationData=FALSE)
plotSANKEY(Object,
                  clusters,
                  validationData=FALSE)

Arguments

`Object`	`ClustAllObject-class` object
`clusters`	Character vector with the names of a pair of stratifications. Check resStratification to obtain the stratification names.
`validationData`	Logical value to use original labelling data to compare with the ClustALL selected stratification.

Value

plot

Examples

data("BreastCancerWisconsin", package = "ClustAll")
label <- as.numeric(as.factor(wdbc$Diagnosis))
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
label <- label[16:30]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)
plotSANKEY(Object = obj_noNA1, clusters = c("cuts_a_1","cuts_b_5"))

obj_noNA1 <- addValidationData(obj_noNA1, label)
plotSANKEY(Object = obj_noNA1, clusters = "cuts_a_1", validationData=TRUE)


data("BreastCancerWisconsin", package = "ClustAll")
label <- as.numeric(as.factor(wdbc$Diagnosis))
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
label <- label[16:30]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)
plotSANKEY(Object = obj_noNA1, clusters = c("cuts_a_1","cuts_b_5"))

obj_noNA1 <- addValidationData(obj_noNA1, label)
plotSANKEY(Object = obj_noNA1, clusters = "cuts_a_1", validationData=TRUE)

Retrieve logical if runClustAll has been executed considering ClustAllObject as input

Description

Generic function to retrieve the logical if runClustAll have been runned from a ClustAllObject-class object

Usage

processed(Object)
processed(Object)

Arguments

Object

ClustAllObject-class object

Value

TRUE if runClustAll has been already executed. Otherwise FALSE

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)
processed(obj_noNA)
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)
processed(obj_noNA)

Show the stratification representatives from the ClustAllObject

Description

This function returns the stratifications representatives by keeping those clusters with a minimum percentage of the population. Default is 0.05. It returns all the robust stratification (TRUE) or the representative for each group of stratifications (FALSE). Default is FALSE

Usage

resStratification(Object,
                         population=0.05,
                         all=FALSE,
                         stratification_similarity=0.7)
resStratification(Object,
                         population=0.05,
                         all=FALSE,
                         stratification_similarity=0.7)

Arguments

`Object`	`ClustAllObject-class` object
`population`	Numeric vector with the minimum percentage of the total population that a stratification must have to be considered as representative
`all`	Logical vector to return all the representative stratifications per group of clusters. If it is FALSE, only the centroid stratification of each group of clusters is returned
`stratification_similarity`	The minimum Jaccard distance value to consider two groups similar. Default is 0.7

Value

list

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)


data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)

ClustAll: Data driven strategy to find hidden subgroups of patients within complex diseases using clinical data

Description

This method runs the ClustAll pipeline

Usage

runClustAll(Object,
                   threads=1,
                   simplify=FALSE)
runClustAll(Object,
                   threads=1,
                   simplify=FALSE)

Arguments

`Object`	`ClustAllObject-class` object
`threads`	Numeric vector that indicates the number of cores to use
`simplify`	if TRUE computes one out of four depths of the dendrogram

Value

An object of class ClustAllObject-class

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]

obj_noNA <- createClustAll(data = wdbc)
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]

obj_noNA <- createClustAll(data = wdbc)
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)

show method for ClustAllObject

Description

Show method for a ClustAllObject-class object

Usage

## S4 method for signature 'ClustAllObject'
show(object)
## S4 method for signature 'ClustAllObject'
show(object)

Arguments

object

ClustAllObject-class object

Value

summarize information about the object

Retrieve the initial data from ClustAllObject

Description

Generic function to retrieve the initial data used for createClustAll from a ClustAllObject-class object

Usage

showData(Object)
showData(Object)

Arguments

Object

ClustAllObject-class object

Value

The Data Frame with the initial data

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_noNA <- createClustAll(data = wdbc, colValidation = "Diagnosis")
showData(obj_noNA)
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=-ID)
obj_noNA <- createClustAll(data = wdbc, colValidation = "Diagnosis")
showData(obj_noNA)

Retrieve the resulting stratifications for each combination of clusterings method (distance + clustering algorithm) and depth from ClustAllObject

Description

Generic function to retrieve the resulting stratifications for each combination of clusterings method (distance + clustering algorithm) and depth of runClustAll from a ClustAllObject-class object

Usage

summary_clusters(Object)
summary_clusters(Object)

Arguments

Object

ClustAllObject-class object

Value

List with the resulting stratifications for each combination of clusterings method (distance + clustering algorithm) and depth methods or NULL if runClustAll method has not been executed yet.

Examples

data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = FALSE)
summary_clusters(obj_noNA1)
data("BreastCancerWisconsin", package = "ClustAll")
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
obj_noNA <- createClustAll(data = wdbc)
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = FALSE)
summary_clusters(obj_noNA1)

validateStratification

Description

Returns the sensitivity and specifity of the selected stratification the original lebelling. The representative stratification names can be obtained using the method resStratification

Usage

validateStratification(Object,
                              stratificationName)
validateStratification(Object,
                              stratificationName)

Arguments

`Object`	`ClustAllObject-class` object
`stratificationName`	Character vector with the name a stratification. Check resStratification to obtain stratification names.

Value

numeric

Examples

data("BreastCancerWisconsin", package = "ClustAll")
label <- as.numeric(as.factor(wdbc$Diagnosis))
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
label <- label[16:30]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)
obj_noNA1 <- addValidationData(Object = obj_noNA1,
                               dataValidation = label)
validateStratification(obj_noNA1, "cuts_a_1")


data("BreastCancerWisconsin", package = "ClustAll")
label <- as.numeric(as.factor(wdbc$Diagnosis))
wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
wdbc <- wdbc[1:15,1:8]
label <- label[16:30]
obj_noNA <- createClustAll(data = wdbc)

obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 1, simplify = TRUE)
resStratification(Object = obj_noNA1, population = 0.05,
                  stratification_similarity = 0.88, all = FALSE)
obj_noNA1 <- addValidationData(Object = obj_noNA1,
                               dataValidation = label)
validateStratification(obj_noNA1, "cuts_a_1")

wdbc: Diagnostic Wisconsin Breast Cancer Database.

Description

A dataset containing Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.

Usage

data("BreastCancerWisconsin", package = "ClustAll")
data("BreastCancerWisconsin", package = "ClustAll")

Format

A data frame with 660 rows and 31 variables

Details

The dataset comprises two types of features —categorical and numerical— derived from a digitized image of a fine needle aspirate (FNA) of a breast mass from 659 patients. Each patient is characterized by 31 features (10x3) and belongs to one of two target classes: ‘malignant’ or ‘benign’.

Value

wdbc dataset

Source

<https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic>

Diagnosis Label says tumor is malingnant or benignant
radius. Mean of distances from the center to points on the perimeter
perimeter
area
smoothness. Local variation in radius lengths
compactness. (Perimeter^2 / Area) - 1.0
concavity. Severity of concave portions of the contour
concave points. Number of concave portions of the contour
symmetry.
fractal dimension. “Coastline approximation” - 1.

wdbcMIDS: Diagnostic Wisconsin Breast Cancer Database with imputed values

Description

We introduced imputed random values to the wdbcNA dataset. Using Mice. It is a mids object. wdbc

Usage

data("BreastCancerWisconsinMISSING", package = "ClustAll")
data("BreastCancerWisconsinMISSING", package = "ClustAll")

Format

A data frame with 660 rows and 31 variables

Value

wdbcMIDS dataset

wdbcNA: Diagnostic Wisconsin Breast Cancer Database with missing values

Description

We introduced random missing values to the wdbc dataset. wdbc

Usage

data("BreastCancerWisconsinMISSING", package = "ClustAll")
data("BreastCancerWisconsinMISSING", package = "ClustAll")

Format

A data frame with 660 rows and 31 variables

Value

wdbcNA dataset

Package 'ClustAll'

Help Index

Add the validation data into the ClustAllObject

Description

Usage

Arguments

Details

Value

See Also

Examples

characterOrNA Class union of character, null or missing

Description

Value

ClustAllObject

Description

Value

Slots

cluster2data

Description

Usage

Arguments

Value

See Also

Examples

Creates ClustAllObject and perform imputations to deal with missing values

Description

Usage

Arguments

Value

See Also

Examples

Retrieve the imputed data from ClustAllObject

Description

Usage

Arguments

Value

See Also

Examples

Retrieve the initial dataOriginal from ClustAllObject

Description

Usage

Arguments

Value

See Also

Examples

Retrieve the original data labelling from ClustAllObject

Description

Usage

Arguments

Value

See Also

Examples

initializeClustAllObject

Description

Usage

Arguments

Value

Retrieve the matrix with the Jaccard distances derived from the robust populations stratifications in ClustAllObject

Description

Usage

Arguments

Value

See Also

Examples

Class Union listOrNULL

Description

Details

Value

logicalOrNA

Description

Details

Value

matrixOrNULL

Description

Details

Value

Retrieve the number of imputations applied at the imputation step from ClustAllObject

Description

Usage

Arguments