Getters for HierarchicalSet objects

Description

These utility functions makes it easy to extract raw information from a HierarchicalSet object.

Usage

sets(x)

clusters(x)

set_names(x)

element_names(x)

n_sets(x)

n_elements(x)

n_clusters(x)

cluster_sizes(x)

set_sizes(x)

cluster_members(x)

set_membership(x)

Arguments

Details

sets Returns a ngCMatrix with sets as columns and elements as rows.

clusters returns a list of dendrograms with the clustering in the HierarchicalSet object

set_names returns a character vector with the names of the sets.

element_names returns a character vector with the names of the elements

n_sets returns the number of sets

n_elements returns the number of elements

n_clusters returns the number of independent set families

cluster_sizes returns the number of sets in each independent set family

set_sizes returns the number of elements in each set

cluster_members returns the members of each independent set family

set_membership returns the cluster each set is member of

Value

depending on the function. See details

Examples

data('twitter')

twitSet <- create_hierarchy(twitter)

# Get the sets as a presence/absence matrix
head(sets(twitSet))

# Get the clustering of the HierarchicalSet object
clusters(twitSet)

# Get the set names
set_names(twitSet)

# Get the element names or NULL if they are unnamed
element_names(twitSet)

# Get the number of sets
n_sets(twitSet)

# Get the number of elements
n_elements(twitSet)

# Get the number of independent clusters
n_clusters(twitSet)

# Get the size of each independent clusters
cluster_sizes(twitSet)

# Get the size of each set
set_sizes(twitSet)

# Get the members of each independent clusters
cluster_members(twitSet)

# Get the membership of each set
set_membership(twitSet)

Create and store hierarchcical sets

Description

HierarchicalSet object can be created using the hSet() constructor. The resulting object will contain both the underlying sets as well as the resulting clustering.

Usage

create_hierarchy(sets, intersectLimit = 1)

## S3 method for class 'HierarchicalSet'
print(x, ...)

## S3 method for class 'HierarchicalSet'
x[[i]]

## S3 method for class 'HierarchicalSet'
x[i]

## S3 method for class 'HierarchicalSet'
sets(x)

## S3 method for class 'HierarchicalSet'
clusters(x)

## S3 method for class 'HierarchicalSet'
set_names(x)

## S3 method for class 'HierarchicalSet'
element_names(x)

## S3 method for class 'HierarchicalSet'
n_sets(x)

## S3 method for class 'HierarchicalSet'
length(x)

## S3 method for class 'HierarchicalSet'
n_elements(x)

## S3 method for class 'HierarchicalSet'
n_clusters(x)

## S3 method for class 'HierarchicalSet'
cluster_sizes(x)

## S3 method for class 'HierarchicalSet'
set_sizes(x)

## S3 method for class 'HierarchicalSet'
cluster_members(x)

## S3 method for class 'HierarchicalSet'
set_membership(x)

Arguments

Details

The HierarchicalSet class contains both the clustering and the original sets. The former is stored in a list of dendrogram objects in and the latter as a presence/absence matrix. Both are retrivable using ⁠$clusters⁠ and ⁠$sets⁠ respectively. Furthermore individual dendrograms can be extracted directly using the [[ operator. If multiple independent clusters exists the object can be subsetted using the [ operator.

For plotting functionality see the separate plot documentation for plot.HierarchicalSet().

Value

An object of class HierarchicalSet

Functions

• print(HierarchicalSet): Print method for HierarchicalSet objects

• [[: Extract dendrogram objects from HierarchicalSet objects

• [: Subset HierarchicalSet object by dendrogram (preserving set information and class)

• sets(HierarchicalSet): Extract the sets as a sparse matrix

• clusters(HierarchicalSet): Extract the clusters as a list of dendrograms

• set_names(HierarchicalSet): Get the names of the sets

• element_names(HierarchicalSet): Get the names of the elements

• n_sets(HierarchicalSet): Get the number of sets

• length(HierarchicalSet): Get the number of sets

• n_elements(HierarchicalSet): Get the number of elements

• n_clusters(HierarchicalSet): Get the number of clusters

• cluster_sizes(HierarchicalSet): Get the size of each clusters

• set_sizes(HierarchicalSet): Get the size of each set

• cluster_members(HierarchicalSet): Get the members of each clusters

• set_membership(HierarchicalSet): Get the membership of each set

Examples

data('twitter')

# Caclulate the clustering
twitSet <- create_hierarchy(twitter)

# Some statistics on the data
n_sets(twitSet)
n_elements(twitSet)
n_clusters(twitSet)

# Focus on the first two independent cluster
twitSet[1:2]

# Extract a dendrogram representation of the firrst cluster
twitSet[[1]]

Parse different set formats into ngCMatrix format

Description

This function is intended to ensure proper formatting of the supplied sets to the create_hierarchy() constructor. Support for other input objects can be created by writting a format_sets method for the class.

Usage

format_sets(x)

Arguments

Value

A presence/absence matrix with sets as columns and elements as rows, formatted as a ngCMatrix

Examples

data('twitter')
format_sets(as.matrix(twitter))

Hierarchical analysis and visualization of set data

Description

This package provides a framework for investigating large scale set data with the use of hierarchical clustering. While hierarchical clustering has been employed on set data numerous times, by converting the presence/absence matrix to a distance matrix and using stats::hclust(), this approach completely removes any notion of underlying set structure from the data. hierarchicalSets instead performs a clustering directly using set algebra by continuously merging sets with the largest intersection (for ties the one with the smallest union is chosen). This structure can then be used in a variety of ways to visualize the relationships between sets. E.g. the intersectionStack plot is a scalable pendant to Venn diagrams (showing the same information but using a different visual mapping).

See Also

create_hierarchy() For constructing HierarchicalSet object and plot.HierarchicalSet() for visualization apporaches.

Create a new hierarchy based on the outlying elements

Description

This function detects the outlying elements of a HierarchicalSet object and creates a new clustering of the sets only based on these elements. The returned HierarchicalSet object will only contain the outlying elements, thus reducing the universe size. This operation is somewhat similar to principal component analysis, in that the derived clustering is based on the structure not captured by the first clustering, thus modeling the second most dominant feature of the data.

Usage

outlier_hierarchy(set, intersectLimit = 1)

Arguments

Value

An object of class HierarchicalSet, based on the outliying elements of set

See Also

outlying_elements() for extracting outlying element information from a HierarchicalSet object

Examples

data('twitter')

twitSet <- create_hierarchy(twitter)
twitSetOut <- outlier_hierarchy(twitSet)
twitSetOut

Extract the outlying elements from each set pair

Description

This function detects the outlying elements of each pair of sets in a HierarchicalSet object. An outlying element is defined as an element in the intersection of the two sets, but not in the intersection of their nearest common set family in the hierarchy.

Usage

outlying_elements(x, counts = TRUE)

Arguments

Value

A data.frame containing information on the outlying elements of each set pair. Only pairs with outlying elements are returned. The 'setX' coloumn contains the index of the first set in the pair and the 'setY' column contains the index of the second set in the pair. If counts = TRUE then the 'nOutliers' column contains the number of outlying elements for each pair. If counts = FALSE the the 'outlier' column contains the index of the outlying elements for each pair

See Also

plot_outlier_distribution() for plotting the distribution of outlying elements in a HierarchicalSet object

Examples

data('twitter')

twitSet <- create_hierarchy(twitter)

# Just get the counts
countOut <- outlying_elements(twitSet)
head(countOut)

# Or the actual elements
elemOut <- outlying_elements(twitSet, FALSE)
head(elemOut)

Visualize hierarchical sets

Description

This is the main visualization interface to HierarchicalSet object. By changing the type argument you control which types of plots are produced. See datails for a walkthrough of the different plot types. All plots are based on ggplot2 but heavily modified using gtable. Because of this the return value is always a gtable object, so it is not possible to add additional geoms, or change scales etc. on the result of plot().

Usage

## S3 method for class 'HierarchicalSet'
plot(
  x,
  label = TRUE,
  type = "dendrogram",
  transform = NULL,
  style = theme_bw(),
  quantiles = 0,
  upperBound = 1,
  tension = 0.8,
  alpha = 1,
  circular = TRUE,
  showHierarchy = !circular,
  evenHierarchy = circular,
  outliers = NULL,
  ratio = NULL,
  n = 50,
  ...
)

Arguments

Details

Currently 4 different plottypes are available:

dendrogram

Plots a horizontal dendrogram with the x-value mapped to the intersection size divided by the union size. This plot very clearly shows the rise in heterogenity as more and more sets are joined, and clearly shows clusters of very similar sets.

intersectStack

Plots a bottom-up icicleplot with height showing the size of the intersection. In essence this plot communicates the same type of information as a Venn-diagram, but in a scalable way and only showing the intersections along the hierarchy. Box color maps to the degree (number of sets) of the intersection making high-degree high-intersection as well as low-degree low-intersection boxes stand out.

heatmap

Plots a traditional heatmap showing all 2-degree intersections. The sets are organized according to the hierarchy so the result should show a number of squares along the diagonal. If two very similar sets have been forced apart by the clustering, this will show up nicely as high value squares away from the diagonal.

composite

Combines dendrogram, intersectStack and heatmap into a composite plot.

outlyingElements

Plots intersects between two sets that are missing from the intersect of their shared top node as hierarchical edge bundles. It helps detect deviations from the global structure as defined by the hierarchcial clustering.

Value

A gtable object invisibly. This function is mainly called for the side effect of creating a plot.

Plot the outlying elements of a HierarchicalSet object

Description

This function creates a scatter plot showing each outlying element as a function of the number of sets it is present in and the number of times it is outlying.

Usage

plot_outlier_distribution(x, alpha = 0.3, outliers = NULL)

Arguments

Value

This function is called for its side effects

See Also

outlying_elements() for extracting outlying element information from a HierarchicalSet object

Examples

data('twitter')

twitSet <- create_hierarchy(twitter)
plot_outlier_distribution(twitSet)

Create a power transformation object

Description

This function can be used to create a proper trans object that encapsulates a power transformation (x^n).

Usage

power_trans(n)

Arguments

Value

A trans object

Followers of 100 twitter users

Description

This dataset captures the followers of 100 highly followed twitter users. The dataset is anonymized and based on the ego-Twitter network from Stanford Large Network Dataset Collection.

Usage

twitter

Format

A ngCMatrix with the sets (users) as columns and elements (followers) as rows

Source

https://snap.stanford.edu/data/