Non-randomness of data

Description

Hopkins.index calculates the Hopkins index that can be used as an indicator of the non-randomness of data prior to clustering.

Usage

Hopkins.index(data)

Arguments

Value

The Hopkins index as a numeric value

See Also

The index is described in, e.g.: Han, Jiawei; Kamber, Micheline (2010): Data mining. Concepts and techniques. 2nd ed., Amsterdam: Elsevier/Morgan Kaufmann (The Morgan Kaufmann series in data management systems).

Examples

## Not run: 
#Random data generation, 10 dimensions, 500 observations, 2 clusters, 
#Multivariate-Bernoulli distributed
require("gtools")
data = c()
p = 0.0
for (i in 1:2)
{
temp = c()
for (j in 1:10)
temp = cbind(temp, rbinom(250, 1, p+(i-1)*0.5+(0.025*i)*j))  
data=rbind(data, temp)
}
data = data[permute(1:500),]

Hopkins.index(data)

## End(Not run)

MBMM clustering

Description

MBM.cluster calculates a model based clustering using multivariate Bernoulli-mixtures as probabilistic model of the data. The quality of the clustering is judged using the AIC criterion.

Usage

MBM.cluster(data, min = 1, max = 10)

Arguments

Value

A list with 3 elements. The first element is the minimal AIC value for each tested number of components. The second element is a vector of all AIC values. The third is the actual clustering as returned by the EM algorithm using the optimal number of components according to AIC. The element is again a list that contains the mixture coefficients, the actual parameters of the mutlivariate Benroulli distributions, the probability matrix of each observation (i.e. row if data) and component and the number of iterations that the EM algorithm needed to converge.

Examples

#Random data generation, 100 observations, 5 dimensions, dependencies within the dimensions
data = cbind(round(runif(100)), round(runif(100)), round(runif(100)))
data = cbind(data, data[,2], 1-data[,3])

#Noisy data:
s = round(runif(2, 1, 100))
data[s, c(4,5)] = 1 - data[s, c(4,5)]

#MBMM Clustering
res = MBM.cluster(data, 1,8)

Similarity based clustering

Description

PAM.cluster calculates a clustering using the PAM algorithm (k-medoids). The quality of the clustering is judged using the G1 index.

Usage

PAM.cluster(data, min = 2, max = 10, metric = "manhattan")

Arguments

Value

A list with 3 elements. The first element contains the optimal number of components according to the G1 index. The second element contains a vector of the G1 values. The thrid element contains the clustering itself, i.e. the return value of PAM.

Examples

## Not run: 
#Random data generation, 10 dimensions, 500 observations, 2 clusters
require("gtools")
data = c()
p = 0.0
for (i in 1:2)
{
temp = c()
for (j in 1:10)
temp = cbind(temp, rbinom(250, 1, p+(i-1)*0.5+(0.025*i)*j))  
data=rbind(data, temp)
}
data = data[permute(1:500),]

PAM.cluster(data)

## End(Not run)

Analyze graph measures of a concept map

Description

analyze.graph.measures analyzes several graph measures. For actual implementations see analyze.graph.measures.conceptmap, or analyze.graph.measures.igraph.

Usage

analyze.graph.measures(x)

Arguments

Value

A list of several graph measures.

Analyzing graph measures of a concept map

Description

analyze.graph.measures analyzes several basic graph measures of a given graph in form of a conceptmap object. All measures are derived by the appropriate functions of igraph.

Usage

## S3 method for class 'conceptmap'
analyze.graph.measures(x)

Arguments

Value

A list with named components that contain the betweenness measure, the edge.connectivity, the diameter, the degree distribution and the communities using the Fastgreedy algorithm.

Examples

require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(15, 0.7, type="gnp"), "name", value=1:15)
analyze.graph.measures(conceptmap(g1))

Analyzing graph measures of an igraph object

Description

analyze.graph.measures.igraph is a convenience method that can be called directly on the result of landscape. It works just like analyze.graph.measures.conceptmap

Usage

## S3 method for class 'igraph'
analyze.graph.measures(x)

Arguments

Value

A list with named components that contain the betweenness measure, the edge.connectivity, the diameter, the degree distribution and the communities using the Fastgreedy algorithm.

Examples

require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(15, 0.7, type="gnp"), "name", value=1:15)
analyze.graph.measures(g1)

Analyzing graph similarity.

Description

analyze.similarity calculates a measure of graph similarity between two concept maps.

Usage

analyze.similarity(map1, map2)

Arguments

Value

A value between 0 and 1 that indicated the structural similariy of the underlying graphs. The graphs need not share the same set of nodes.

See Also

The structural similarity that is calculated is described in: Goldsmith, Timothy E.; Davenport, Daniel M. (1990): Assessing Structural Similarity of Graphs. In: Roger W. Schvaneveldt (Hg.): Pathfinder associative networks. Studies in knowledge organizations. Norwood, N.J: Ablex Pub. Corp., S. 74-87.

Examples

require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(15, 0.7, type="gnp"), "name", value=1:15)
g2 = set.vertex.attribute(erdos.renyi.game(15, 0.7, type="gnp"), "name", value=1:15)
analyze.similarity(conceptmap(g1), conceptmap(g2))

Analyzing small subgraph patterns.

Description

analyze.subgraphs analyzes the frequency of subgraph patterns given a list of concepts and a set of maps in a conceptmaps object.

Usage

analyze.subgraphs(maps, concept.list)

Arguments

Value

A list with two elements. The first is a vector that contains the absolute number of occurrences for each subgraph pattern. The second element is a list of igraph objects of the pattern themselves.

Examples

#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

#Create conceptmaps object from three conceptmap objects
simple_cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))

analyze.subgraphs(simple_cms, c("1", "2", "3"))

Convert a conceptmap object to a matrix

Description

as.matrix converts a conceptmap object into a matrix. The output can be fed back into conceptmap.matrix.

Usage

## S3 method for class 'conceptmap'
as.matrix(x, ...)

Arguments

Value

A matrix with 3 columns and one row for each proposition of the concept map. The 3 columns contain the start and end node of each proposition as well as the label of the edge (as character vectors).

Convert a conceptmaps object to a matrix

Description

as.matrix converts a conceptmaps object into a matrix. The output can be fed back into conceptmaps.matrix.

Usage

## S3 method for class 'conceptmaps'
as.matrix(x, ...)

Arguments

Value

A matrix with 4 columns and one row for each proposition of one of the concept maps. The 4 columns contain an id of the map (starting from 1) and then the start and end node of each proposition as well as the label of the edge (as character vectors).

Examples

data = rbind(
 cbind("1", "Object", "Class", "is instance of"),
 cbind("1", "Object", "Attribute", "has"),
 cbind("2", "Class", "Attribute", "possesses"),
 cbind("2", "Attribute", "Data-type", "has"),
 cbind("3", "Object", "Class", "is instance of")
 )
 cms = conceptmaps(data)
 as.matrix(cms) 

Clustering maps of a conceptmaps object

Description

clustering is a convenience function that implements two frequently used ways of clustering conceptmaps directly. The first is clustering using the MBMM algorithm and the concept matrix, the second is clustering using the PAM algorithm and the graph similarity matrix.

Usage

clustering(maps, method = c("MBMM", "PAM"), min = 1, max = 10)

Arguments

Value

The return value of either MBM.cluster or PAM.cluster, depending on the value of method.

Examples

## Not run: 
#Assuming that there are concept maps in folder "~/maps"
cms = read.folder.tgf("~/maps")

clustering(cms, method="MBMM")

## End(Not run)

Forming the concept vector of a conceptmap object

Description

concept.vector transforms a concept map into a numeric vector that contains for each occuring concept the number of adjacent edges.

Usage

concept.vector(x)

Arguments

Value

A numeric vector. The columns are named after the concepts and sorted alphabetically.

Examples

#Create concept map from a random graph
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
cm = conceptmap(g1)

concept.vector(cm)

Constructing a conceptmap object

Description

conceptmap creates an object that encompasses a concept maps. For actual implementations, see conceptmap.default, conceptmap.igraph, or conceptmap.matrix.

Usage

conceptmap(x, ...)

Arguments

Value

A conceptmap object.

Basic creation of a conceptmap object

Description

conceptmap creates a conceptmap object based on either an empty concept map or on (matrix) data.

Usage

## Default S3 method:
conceptmap(x, ...)

Arguments

Value

A conceptmap object.

Examples

empty_cm = conceptmap(NULL)

Creation of a conceptmap object from an existing graph

Description

conceptmap takes an existing igraph object and tries of coerce it into a conceptmap object (encompassing the igraph object).

Usage

## S3 method for class 'igraph'
conceptmap(x, strip = TRUE, ...)

Arguments

Value

A conceptmap object.

Examples

#Create conceptmap from a complete graph with 5 nodes
require("igraph")
graph = graph.full(5)
graph = set.vertex.attribute(graph, "name", value=1:5)
simple_cm = conceptmap(graph)

Creation of a conceptmap object from matrix data

Description

conceptmap creates a conceptmap object from a given matrix of a particular format (see below).

Usage

## S3 method for class 'matrix'
conceptmap(x, ...)

Arguments

Value

A conceptmap object.

Examples

data = rbind(cbind("Object", "Class", "is instance of"), cbind("Class", "Attribute", "has"))
cm = conceptmap(data)

Constructing a conceptmaps object

Description

conceptmaps creates an object that encompasses a set of concept maps. For actual implementations, see conceptmaps.default, conceptmaps.list, conceptmaps.matrix.

Usage

conceptmaps(x, ...)

Arguments

Value

A conceptmaps object.

Basic creation of a conceptmaps object

Description

conceptmaps creates a conceptmaps object based on either an empty set of concept map or on a list of concept maps.

Usage

## Default S3 method:
conceptmaps(x, ...)

Arguments

Value

A conceptmaps object.

Examples

empty_cms = conceptmaps(NULL)

Creation of a conceptmaps object from a list

Description

conceptmaps creates a conceptmaps object from a list of conceptmap objects.

Usage

## S3 method for class 'list'
conceptmaps(x, filter = T, ...)

Arguments

Value

A conceptmaps object.

Examples

#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

#Create conceptmaps object from three conceptmap objects
simple_cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))

Creation of a conceptmaps object from a matrix

Description

conceptmaps creates a conceptmaps object from a set of concept maps represented as a matrix.

Usage

## S3 method for class 'matrix'
conceptmaps(x, filter = T, ...)

Arguments

Value

A conceptmaps object.

Examples

data = rbind(
 cbind("1", "Object", "Class", "is instance of"),
 cbind("1", "Object", "Attribute", "has"),
 cbind("2", "Class", "Attribute", "possesses"),
 cbind("2", "Attribute", "Data-type", "has"),
 cbind("3", "Object", "Class", "is instance of")
 )
 cms = conceptmaps(data)

Forming the edge vector of a conceptmap object

Description

edge.vector transforms a concept map into a numeric vector that contains for each occuring pair of concepts whether or not this pair is connected by a proposition in the concept map.

Usage

edge.vector(x)

Arguments

Value

A numeric vector. The columns are named after the concept-pairs which are sorted alphabetically.

Examples

#Create concept map from a random graph
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
cm = conceptmap(g1)

edge.vector(cm)

Finding all concepts used.

Description

get.unified.concepts identifies the common superset of concepts that is used by the maps of a conceptmaps object.

Usage

get.unified.concepts(maps)

Arguments

Value

A vector of strings of each concepts that appears in at least one of the maps of maps.

Aggregating the maps of a conceptmaps object into a concept landscape

Description

landscape transforms a set of concept maps into a concept landscape using one of several possible methods. Depending on the value of result and accumulation or amalgamation is performed on the concept map data. The amalgamation forms a weighted graph based on the unified set of concepts. An accumulation transforms each concept map into a vector and returns a matrix of these vectors. Using FUN the process of transformation can be influenced in both cases.

Usage

landscape(maps, result = c("graph", "matrix"), mode, FUN = NULL)

Arguments

Value

Depending on result either an igraph object or a numeric matrix.

Examples

#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

#Create conceptmaps object from three conceptmap objects
cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))

landscape(cms, result="graph", mode="undirected")

landscape(cms, result="matrix", mode="concept.vector")

Unify sets of conceptmaps

Description

merge takes two conceptsmaps objects and merges the underyling sets of conceptmaps.

Usage

## S3 method for class 'conceptmaps'
merge(x, y, ...)

Arguments

Value

A conceptmaps object that consist of the maps of x and y.

Examples

 data = rbind(
 cbind("1", "Object", "Class", "is instance of"),
 cbind("1", "Object", "Attribute", "has"),
 cbind("2", "Class", "Attribute", "possesses"),
 cbind("2", "Attribute", "Data-type", "has"),
 cbind("3", "Object", "Class", "is instance of")
 )
 cm1 = conceptmaps(data[1:2,])
 cm2 = conceptmaps(data[3:5,])
 merge(cm1, cm2)

Modify the concepts of concept maps

Description

modify.concepts modifies the list of concept of a concept map or of all maps of a set. For actual implementations see modify.concepts.conceptmap, or modify.concepts.conceptmaps.

Usage

modify.concepts(x, concept.list, ...)

Arguments

Value

A conceptmaps or conceptmap object.

Adapt list of concepts of a conceptmap object

Description

modify.concepts modifes the list of concepts according to a given list. This includes removing concepts and adjacent propositions as well as adding (unconnected) concepts.

Usage

## S3 method for class 'conceptmap'
modify.concepts(x, concept.list, ...)

Arguments

Value

A conceptmap object that encompasses exactly the concepts of concept.list. Concepts not originally in map are added as isolated nodes/concepts. Concepts of map that are not in concept.list are removed together with their adjacent propositions.

Examples

data = rbind(cbind("Object", "Class", "is instance of"), cbind("Class", "Attribute", "has"))
cm = conceptmap(data)
modify.concepts(cm, c("Object", "Class", "Method"))

Modifying the concepts of all maps of a conceptmaps object.

Description

modify.concepts calls modify.concepts.conceptmap for each conceptmap object of a conceptmaps object. Therefore, all concept maps will share the same set of concepts afterwards.

Usage

## S3 method for class 'conceptmaps'
modify.concepts(x, concept.list, filter = F, ...)

Arguments

Value

A conceptmaps object that contains (possibly a subset of) the maps of maps in which every map contains the concepts of concept.list.

Examples

 data = rbind(
 cbind("1", "Object", "Class", "is instance of"),
 cbind("1", "Object", "Attribute", "has"),
 cbind("2", "Class", "Attribute", "possesses"),
 cbind("2", "Attribute", "Data-type", "has"),
 cbind("3", "Object", "Class", "is instance of")
 )
 cms = conceptmaps(data)
 
 modify.concepts(cms, c("Object", "Class"), filter=TRUE)

Construct a Pathfinder network from a conceptmap or a concept landscape

Description

pathfinder creates Pathfinder network. For more information and actual implementations see pathfinder.matrix, pathfinder.conceptmaps, or pathfinder.igraph.

Usage

pathfinder(data, q, r, ...)

Arguments

Value

The Pathfinder network of the input data.

Creating a Pathfinder network from a conceptmaps object

Description

pathfinder creates the Pathfinder network from a given set of conceptmaps. The conecpts of each concept map are unified, then the concept maps are transformed into a weight matrix and pathfinder.matrix is called on the data.

Usage

## S3 method for class 'conceptmaps'
pathfinder(data, q = 2, r = 1, threshold = 0,
  directed = F, prune.edges = F, return.cm = F, filename = "", ...)

Arguments

Value

Depending on return.cm either an igraph object or a conceptmap object that represents the Pathfinder network. If an igraph object is returned, the graph will be weighted.

Examples

#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

#Create conceptmaps object from three conceptmap objects
simple_cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))

#Create Pathfinder network from data and return a conceptmap object
cm = pathfinder(simple_cms, q=1, return.cm=TRUE)

Creating a Pathfinder network from an igraph object

Description

pathfinder creates the Pathfinder network from a weighted graph based on pathfinder.matrix. It is a convenience method that can be called on the result of a call to landscape

Usage

## S3 method for class 'igraph'
pathfinder(data, q = 2, r = 1, threshold = 0,
  prune.edges = F, filename = "", ...)

Arguments

Value

An igraph object that represents the Pathfinder network as a weighted graph.

Examples

#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

#Create conceptmaps object from three conceptmap objects
simple_cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))
pathfinder(landscape(simple_cms, result="graph", mode="undirected"))

Creating a Pathfinder network from a matrix

Description

pathfinder creates the Pathfinder network from a given weight matrix.

Usage

## S3 method for class 'matrix'
pathfinder(data, q, r, ...)

Arguments

Value

A numeric weight matrix that represented the Pathfinder graph of the input graph.

See Also

The Pathfinder algorithm is implemented based on the description in: Dearholt, Donald W.; Schvaneveldt, Roger W. (1990): Properties of Pathfinder Netowrks. In: Roger W. Schvaneveldt (Hg.): Pathfinder associative networks. Studies in knowledge organizations. Norwood, N.J: Ablex Pub. Corp., S. 1-30.

Examples

#Manually create a weighted graph
data = matrix(data = 0, nrow = 6, ncol=6)
colnames(data) <- c("Object", "Class", "Method", "Attribute", "Visibility", "Algorithm")
rownames(data) <- c("Object", "Class", "Method", "Attribute", "Visibility", "Algorithm")
data["Object", "Class"] = 3
data["Object", "Method"] = 3
data["Object", "Attribute"] = 10
data["Object", "Visibility"] = Inf
data["Object", "Algorithm"] = 9
data["Class", "Method"] = 7
data["Class", "Attribute"] = 6
data["Class", "Visibility"] = 8
data["Class", "Algorithm"] = 10
data["Method", "Attribute"] = 4
data["Method", "Visibility"] = 9
data["Method", "Algorithm"] = 3
data["Attribute", "Visibility"] = 5
data["Attribute", "Algorithm"] = 10
data["Visibility", "Algorithm"] = Inf

data = data + t(data)

#Run the Pathfinder algorithm with several different parameters
pathfinder(data, 5, 1)
pathfinder(data, 2, 1)
pathfinder(data, 5,Inf)
pathfinder(data, 2, Inf)

Plotting a conceptmap

Description

plot plots a concept map. Includes finding a good layout based on communities and a circular layout. Is especially suited for plotting larger concept maps, in particular amalgamations.

Usage

## S3 method for class 'conceptmap'
plot(x, edge.labels = T, max.label.len = 25,
  scale = 1, layout = NULL, ...)

Arguments

Value

-

Examples

#Create concept map from a random graph
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
E(g1)$name <- rep("", length(E(g1)))
plot(conceptmap(g1), edge.labels=FALSE, layout="kamada.kawai")

Plotting a series of concept maps

Description

plot plots a set of concept maps. The layout is determined based on the union of all concept maps, then each map is individually plotted using this fixed layout. Is escpecially useful for visualizing horizontal landscapes.

Usage

## S3 method for class 'conceptmaps'
plot(x, edge.labels = T, max.label.len = 25,
  scale = 1, layout = NULL, ...)

Arguments

Value

-

Examples

#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
E(g1)$name <- rep("", length(E(g1)))
E(g2)$name <- rep("", length(E(g2)))
E(g3)$name <- rep("", length(E(g3)))
#Create conceptmaps object from three conceptmap objects
simple_cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))

plot(simple_cms, layout="spring")

Display basic information of a conceptmap object

Description

print displays basic information. For plotting, see plot.conceptmap

Usage

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

Arguments

Value

-

Display basic information of a conceptmaps object

Description

print displays basic information. For plotting, see plot.conceptmaps

Usage

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

Arguments

Value

-

Importing a set of concept maps from TGF files.

Description

read.folder.tgf reads several TGF files and imports them as a conceptmaps object.

Usage

read.folder.tgf(folder, strip = TRUE)

Arguments

Value

A list consisting of a conceptmaps object and the list of filenames (in the same order as the maps in the conceptmaps object).

Examples

## Not run: 
#Assuming that the data is in the folder "~/cmaps"
cm = read.folder.tgf("~/cmaps")

## End(Not run)

Importing a set of concept maps from GraphML files.

Description

read.folder.yEd reads several graphML files that were created by yEd and imports them as a conceptmaps object.

Usage

read.folder.yEd(folder, strip = TRUE)

Arguments

Value

A list consisting of a conceptmaps object and the list of filenames (in the same order as the maps in the conceptmaps object).

Examples

## Not run: 
#Assuming that the data is in the folder "~/cmaps"
cm = read.folder.yEd("~/cmaps")

## End(Not run)

Importing a concept map from a TGF file.

Description

read.tgf reads a TGF file and imports the graph as a conceptmap object.

Usage

read.tgf(file, strip = TRUE)

Arguments

Value

A conceptmap object.

Examples

## Not run: 
#Assuming that the data is in "~/cmap.tgf"
cm = read.tgf("~/cmap.tgf")

## End(Not run)

Importing a concept map from a GraphML file.

Description

read.yEd reads a graphML file that was created by yEd and imports the graph as a conceptmap object.

Usage

read.yEd(file, strip = TRUE)

Arguments

Value

A conceptmap object.

Examples

## Not run: 
#Assuming that the data is in "~/cmap.graphml"
cm = read.yEd("~/cmap.graphml")

## End(Not run)

Select a subset of a set of conceptmaps

Description

splice selects a subset of a set of concept maps and returns them as a new conceptmaps object.

Usage

splice(maps, keep)

Arguments

Value

A conceptmaps object that consist of the maps with indiced of maps.

Examples

 data = rbind(
 cbind("1", "Object", "Class", "is instance of"),
 cbind("1", "Object", "Attribute", "has"),
 cbind("2", "Class", "Attribute", "possesses"),
 cbind("2", "Attribute", "Data-type", "has"),
 cbind("3", "Object", "Class", "is instance of")
 )
 cms = conceptmaps(data)
 
 splice(cms, c(1,3))

Return basic information of a conceptmap object

Description

summary returns basic information about a conceptmap object

Usage

## S3 method for class 'conceptmap'
summary(object, ...)

Arguments

Value

A list with the number of concepts, edges and components of the concept map.

Return basic information of a conceptmaps object

Description

summary returns basic information about a conceptmaps object

Usage

## S3 method for class 'conceptmaps'
summary(object, ...)

Arguments

Value

A matrix with one column for each concept map in the set and the number of concepts, edges, and components of this map respectively in 3 rows.

Unifying the concepts of a conceptmap object.

Description

unify.concepts first calls get.unified.concepts on the maps of a conceptmaps object and then calls modify.concepts.conceptmaps on each of the constituent maps. Afterwards, therefore, each map of the conceptmaps object will share the same common superset of concepts.

Usage

unify.concepts(maps)

Arguments

Value

A conceptmaps object in of the same map of maps, in which every map shares the same concepts.

Examples

 data = rbind(
 cbind("1", "Object", "Class", "is instance of"),
 cbind("1", "Object", "Attribute", "has"),
 cbind("2", "Class", "Attribute", "possesses"),
 cbind("2", "Attribute", "Data-type", "has"),
 cbind("3", "Object", "Class", "is instance of")
 )
 cms = conceptmaps(data)
 
 unify.concepts(cms)

Saving a concept map to a TGF file

Description

write.tgf stores the graph underlying a conceptmap object into a file using the "Trivial Grpah Format" (TGF).

Usage

write.tgf(map, file, translation = NULL)

Arguments

Value

-

Examples

## Not run: 
#Create concept map from a random graph
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

write.tgf(conceptmap(g1), "~/cmap.tgf", 
          translation = c("Node_1", "Node_2", "Node_3", "Node_4", "Node_5"))

## End(Not run)

Saving a set of concept maps to TGF files

Description

write.tgf.folder stores the graphs underlying the maps of a conceptmaps object into a folder using the "Trivial Grpah Format" (TGF). The function calls write.tgf for each of the maps of a conceptmaps object. The files will be named "1.tgf", "2.tgf" and so on.

Usage

write.tgf.folder(maps, folder, translation = NULL)

Arguments

Value

-

Examples

## Not run: 
#Create concept maps from three random graphs
require("igraph")
g1 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g2 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)
g3 = set.vertex.attribute(erdos.renyi.game(5, 0.7, type="gnp"), "name", value=1:5)

#Create conceptmaps object from three conceptmap objects
simple_cms = conceptmaps(list(conceptmap(g1), conceptmap(g2), conceptmap(g3)))

write.tgf.folder(simple_cms, "~/cmaps")

## End(Not run)