FormalContext: R6 class for a formal context
In fcaR: Formal Concept Analysis
FormalContext R Documentation
R6 class for a formal context
Description
This class implements the data structure and methods for formal contexts.
Public fields
IThe table of the formal context as a matrix.
attributesThe attributes of the formal context.
objectsThe objects of the formal context.
grades_setThe set of degrees (in [0, 1]) the whole set of attributes can take.
expanded_grades_setThe set of degrees (in [0, 1]) each attribute can take.
conceptsThe concept lattice associated to the formal context as a ConceptLattice.
implicationsA set of implications on the formal context as an ImplicationSet.
descriptionAn optional description of the dataset
Methods
Public methods
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Method new()
Creator for the Formal Context class
Usage
FormalContext$new(I, filename, remove_const = FALSE)
Arguments
I(numeric matrix) The table of the formal context.
filename(character) Path of a file to import.
remove_const(logical) If TRUE, remove constant columns. The default is FALSE.
Details
Columns of I should be named, since they are the names of the attributes of the formal context.
If no I is used, the resulting FormalContext will be empty and not usable unless for loading a previously saved one. In this case, one can provide a filename to import. Only RDS, CSV and CXT files are currently supported.
If the file is not present, the fcarepository.org is looked for coincidences. If so, the corresponding context is loaded.
Returns
An object of the FormalContext class.
Method is_empty()
Check if the FormalContext is empty
Usage
FormalContext$is_empty()
Returns
TRUE if the FormalContext is empty, that is, has not been provided with a matrix, and FALSE otherwise.
Method scale()
Scale the context
Usage
FormalContext$scale(attributes, type, ...)
Arguments
attributesThe attributes to scale
typeType of scaling.
...
Details
The types of scaling are implemented in a registry,
so that scalingRegistry$get_entries() returns
all types.
In the dots argument, the user can supply the value for bg (logical), which, if set to TRUE, indicates to compute background knowledge as implications on the scales; if FALSE, no implications will be computed on the scales.
Returns
The scaled formal context
Examples
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
Method get_scales()
Scales applied to the formal context
Usage
FormalContext$get_scales(attributes = names(private$scales))
Arguments
attributes(character) Name of the attributes for which scales
(if applied) are returned.
Returns
The scales that have been applied to the specified attributes
of the formal context. If no attributes are passed,
then all applied scales are returned.
Examples
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$get_scales()
Method background_knowledge()
Background knowledge of a scaled formal context
Usage
FormalContext$background_knowledge()
Returns
An ImplicationSet with the implications
extracted from the application of scales.
Examples
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$background_knowledge()
Method dual()
Get the dual formal context
Usage
FormalContext$dual()
Returns
A FormalContext where objects and attributes have interchanged their roles.
Method intent()
Get the intent of a fuzzy set of objects
Usage
FormalContext$intent(...)
Arguments
...A Set of objects, or object names.
Returns
A Set with the intent.
Method uparrow()
Get the intent of a fuzzy set of objects
Usage
FormalContext$uparrow(S)
Arguments
S(Set) The set of objects to compute the intent for.
Returns
A Set with the intent.
Method extent()
Get the extent of a fuzzy set of attributes
Usage
FormalContext$extent(...)
Arguments
...A Set of attributes, or attribute names.
Returns
A Set with the intent.
Method downarrow()
Get the extent of a fuzzy set of attributes
Usage
FormalContext$downarrow(S)
Arguments
S(Set) The set of attributes to compute the extent for.
Returns
A Set with the intent.
Method closure()
Get the closure of a fuzzy set of attributes
Usage
FormalContext$closure(...)
Arguments
...A Set of attributes, or attribute names.
Returns
A Set with the closure.
Method obj_concept()
Object Concept
Usage
FormalContext$obj_concept(object)
Arguments
object(character) Name of the object to compute its associated concept
Returns
The object concept associated to the object given.
Method att_concept()
Attribute Concept
Usage
FormalContext$att_concept(attribute)
Arguments
attribute(character) Name of the attribute to compute its associated concept
Returns
The attribute concept associated to the attribute given.
Method is_concept()
Is a Concept?
Usage
FormalContext$is_concept(C)
Arguments
CA Concept object
Returns
TRUE if C is a concept.
Method is_closed()
Testing closure of attribute sets
Usage
FormalContext$is_closed(...)
Arguments
...A Set of attributes, or attribute names.
Returns
TRUE if the set S is closed in this formal context.
Method clarify()
Clarify a formal context
Usage
FormalContext$clarify(copy = FALSE)
Arguments
copy(logical) If TRUE, a new FormalContext object is created with the clarified context, otherwise the current one is overwritten.
Returns
The clarified FormalContext.
Method reduce()
Reduce a formal context
Usage
FormalContext$reduce(copy = FALSE, method = c("arrows", "concepts"))
Arguments
copy(logical) If TRUE, a new FormalContext object is created with the clarified and reduced context, otherwise the current one is overwritten.
method(character) The method to use for reduction. One of "arrows" (default, uses arrow relations, only for binary contexts) or "concepts" (uses irreducible concepts, works for fuzzy).
Returns
The clarified and reduced FormalContext.
Method standardize()
Build the Standard Context
Usage
FormalContext$standardize()
Details
This is a wrapper around reduce(copy = TRUE, method = "arrows") for binary contexts,
or reduce(copy = TRUE, method = "concepts") for fuzzy contexts.
Returns
The standard context using the join- and meet- irreducible elements.
Method find_concepts()
Use Ganter Algorithm to compute concepts
Usage
FormalContext$find_concepts(method = "InClose", verbose = FALSE)
Arguments
method(string) The name of a method for the computation of concepts. Available options can be listed with conceptRegistry$get_entries().
verbose(logical) TRUE will provide a verbose output.
Returns
A list with all the concepts in the formal context.
Method find_implications()
Use modified Ganter algorithm to compute both concepts and implications
Usage
FormalContext$find_implications(
save_concepts = TRUE,
verbose = FALSE,
method = c("LinCbO", "NextClosure")
)
Arguments
save_concepts(logical) TRUE will also compute and save the concept lattice. FALSE is usually faster, since it only computes implications.
verbose(logical) TRUE will provide a verbose output.
method(character) Algorithm to use for binary contexts. One of "LinCbO" (default, Janostik, Konecny, Krajca) or "NextClosure" (Ganter's algorithm). For non-binary (fuzzy) contexts, NextClosure is always used.
Returns
Nothing, just updates the internal fields concepts and implications.
Method find_protoconcepts()
Find protoconcepts
Usage
FormalContext$find_protoconcepts(verbose = FALSE)
Arguments
verbose(logical) Show verbose output.
Returns
A list of protoconcepts, where each protoconcept is a list of two Sets (extent and intent).
Method find_causal_rules()
Find causal rules
Usage
FormalContext$find_causal_rules(
response_var,
min_support = 0.1,
confidence_level = 0.95,
max_length = 3,
verbose = FALSE
)
Arguments
response_var(character) The name of the response variable.
min_support(numeric) Minimum support for the premise attributes.
confidence_level(numeric) Confidence level for the causality test.
max_length(integer) Maximum length of the premise.
verbose(logical) Show verbose output.
Returns
A RuleSet object containing the discovered causal rules and their quality metrics.
Method factorize()
Factorize the formal context using Boolean Matrix Factorization (BMF) algorithms.
Note: Fuzzy contexts are currently not supported and will result in an error.
Usage
FormalContext$factorize(method = "GreConD", ...)
Arguments
method(character) The algorithm to use. Supported algorithms:
"RSF", "RSF-ES", "GreConD", "GreEss", "ASSO", "PaNDa+ MDL", "PaNDa+ ASSO-style",
"PaNDa+ Weighted", "Hyper", "Hyper+".
...Additional arguments depending on the method:
-
GreConD, PaNDa+ *, ASSO: k (integer) maximum factors to extract.
-
ASSO: threshold (default 0.6), w_pos (default 1.0), w_neg (default 1.0).
-
PaNDa+ Weighted: rho (default 1.0).
-
Hyper, Hyper+: min_support (ratio, default 0.05).
-
Hyper+: beta (default 0.1).
Returns
A list with two FormalContext objects:
-
object_factor: The context mapping Objects to Factors (Matrix A / U).
-
factor_attribute: The context mapping Factors to Attributes (Matrix B / V).
Method to_transactions()
Convert the formal context to object of class transactions from the arules package
Usage
FormalContext$to_transactions()
Returns
A transactions object.
Method save()
Save a FormalContext to RDS or CXT format
Usage
FormalContext$save(filename = tempfile(fileext = ".rds"))
Arguments
filename(character) Path of the file where to store the FormalContext.
Details
The format is inferred from the extension of the filename.
Returns
Invisibly the current FormalContext.
Method load()
Load a FormalContext from a file
Usage
FormalContext$load(filename)
Arguments
filename(character) Path of the file to load the FormalContext from.
Details
Currently, only RDS, CSV and CXT files are supported.
Returns
The loaded FormalContext.
Method dim()
Dimensions of the formal context
Usage
FormalContext$dim()
Returns
A vector with (number of objects, number of attributes).
Method to_json()
Export the formal context to JSON
Usage
FormalContext$to_json(file = NULL)
Arguments
file(character) The path of the file to save the JSON to.
Returns
A JSON string representing the formal context.
Method print()
Prints the formal context
Usage
FormalContext$print()
Returns
Prints information regarding the formal context.
Method calculate_arrow_relations()
Calculate arrow relations for a binary context.
Usage
FormalContext$calculate_arrow_relations()
Details
This method computes the arrow relations (swarrow, nearrow, and double arrow).
Returns
Nothing, updates the internal state.
Method get_arrow_relations()
Get arrow relations
Usage
FormalContext$get_arrow_relations()
Returns
An integer matrix with the arrow relations.
Method get_irreducible_objects()
Get irreducible objects
Usage
FormalContext$get_irreducible_objects()
Returns
A character vector with the names of the irreducible objects.
Method get_irreducible_attributes()
Get irreducible attributes
Usage
FormalContext$get_irreducible_attributes()
Returns
A character vector with the names of the irreducible attributes.
Method is_distributive()
Check if the context (and its lattice) is distributive
Usage
FormalContext$is_distributive()
Returns
Logical: TRUE if the lattice is distributive.
Method reduce_arrows()
Reduce the formal context using arrow relations
Usage
FormalContext$reduce_arrows()
Returns
A new FormalContext object that is clarified and reduced.
Method get_core()
Get the Core of the Formal Context
Usage
FormalContext$get_core()
Details
The core is the minimal subcontext that generates the same concept lattice.
For binary contexts, this is equivalent to standardize().
Returns
A new FormalContext object containing only irreducible rows and columns.
Method to_latex()
Write the context in LaTeX format
Usage
FormalContext$to_latex(table = TRUE, label = "", caption = "")
Arguments
table(logical) If TRUE, surrounds everything between \begin{table} and \end{table}.
label(character) The label for the table environment.
caption(character) The caption of the table.
fraction(character) If none, no fractions are produced. Otherwise, if it is frac, dfrac or sfrac, decimal numbers are represented as fractions with the corresponding LaTeX typesetting.
Returns
A table environment in LaTeX.
Method incidence()
Incidence matrix of the formal context
Usage
FormalContext$incidence()
Returns
The incidence matrix of the formal context
Examples
fc <- FormalContext$new(planets)
fc$incidence()
Method subcontext()
Generates a new FormalContext restricted to a subset of objects and/or attributes.
Usage
FormalContext$subcontext(objects, attributes)
Arguments
objects(character or integer vector) The names or indices of the objects to keep. If NULL, keeps all.
attributes(character or integer vector) The names or indices of the attributes to keep. If NULL, keeps all.
Returns
A new FormalContext object representing the subcontext.
Examples
fc <- FormalContext$new(planets)
fc$subcontext(attributes = c("moon", "no_moon"))
Method [()
Subcontext of the formal context
Usage
FormalContext$[(objects, attributes)
Arguments
objects(character array) Name of the objects to
keep.
attributes(character array) Names of the attributes
to keep.
Details
A warning will be issued if any of the names is not present
in the list of objects or attributes of the formal context.
If objects or attributes is empty, then it is
assumed to represent the whole set of objects or attributes
of the original formal context.
Returns
Another FormalContext that is a subcontext
of the original one, with only the objects and attributes
selected.
Examples
fc <- FormalContext$new(planets)
fc[, c("moon", "no_moon")]
Method plot()
Plot the formal context table
Usage
FormalContext$plot(to_latex = FALSE, ...)
Arguments
to_latex(logical) If TRUE, export the plot as a tikzpicture environment that can be included in a LaTeX file.
...Other parameters to be passed to the tikzDevice that renders the lattice in LaTeX, or for the figure caption. See Details.
Details
Particular parameters that control the size of the tikz output are: width, height (both in inches), and pointsize (in points), that should be set to the font size used in the documentclass header in the LaTeX file where the code is to be inserted.
If a caption is provided, the whole tikz picture will be wrapped by a figure environment and the caption set.
Returns
If to_latex is FALSE, it returns nothing, just plots the graph of the formal context. Otherwise, this function returns the LaTeX code to reproduce the formal context plot.
Method use_logic()
Sets the logic to use
Usage
FormalContext$use_logic(name = available_logics())
Arguments
nameThe name of the logic to use. To see the available names, run available_logics().
Method get_logic()
Gets the logic used
Usage
FormalContext$get_logic()
Returns
A string with the name of the logic.
Method use_connection()
Sets the name of the Galois connection to use
Usage
FormalContext$use_connection(connection)
Arguments
connectionThe name of the Galois connection. Available connections are "standard" (antitone), "benevolent1" and "benevolent2" (isotone)
Method get_connection()
Gets the name of the Galois connection
Usage
FormalContext$get_connection()
Returns
A string with the name of the Galois connection
Method clone()
The objects of this class are cloneable with this method.
Usage
FormalContext$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
References
Guigues J, Duquenne V (1986). “Familles minimales d'implications informatives résultant d'un tableau de données binaires.” Mathématiques et Sciences humaines, 95, 5-18.
Ganter B, Wille R (1999). Formal concept analysis : mathematical foundations. Springer. ISBN 3540627715.
Belohlavek R (2002). “Algorithms for fuzzy concept lattices.” In Proc. Fourth Int. Conf. on Recent Advances in Soft Computing. Nottingham, United Kingdom, 200-205.
Hahsler M, Grun B, Hornik K (2005). “arules - a computational environment for mining association rules and frequent item sets.” J Stat Softw, 14, 1-25.
Examples
# Build and print the formal context
fc_planets <- FormalContext$new(planets)
print(fc_planets)
# Define a set of attributes
S <- Set$new(attributes = fc_planets$attributes)
S$assign(moon = 1, large = 1)
# Compute the closure of S
Sc <- fc_planets$closure(S)
# Is Sc a closed set?
fc_planets$is_closed(Sc)
# Clarify and reduce the formal context
fc2 <- fc_planets$reduce(TRUE)
# Find implications
fc_planets$find_implications()
# Read a formal context from CSV
filename <- system.file("contexts", "airlines.csv", package = "fcaR")
fc <- FormalContext$new(filename)
# Read a formal context from a CXT file
filename <- system.file("contexts", "lives_in_water.cxt", package = "fcaR")
fc <- FormalContext$new(filename)
## ------------------------------------------------
## Method `FormalContext$scale`
## ------------------------------------------------
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
## ------------------------------------------------
## Method `FormalContext$get_scales`
## ------------------------------------------------
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$get_scales()
## ------------------------------------------------
## Method `FormalContext$background_knowledge`
## ------------------------------------------------
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$background_knowledge()
## ------------------------------------------------
## Method `FormalContext$incidence`
## ------------------------------------------------
fc <- FormalContext$new(planets)
fc$incidence()
## ------------------------------------------------
## Method `FormalContext$subcontext`
## ------------------------------------------------
fc <- FormalContext$new(planets)
fc$subcontext(attributes = c("moon", "no_moon"))
## ------------------------------------------------
## Method `FormalContext$[`
## ------------------------------------------------
fc <- FormalContext$new(planets)
fc[, c("moon", "no_moon")]
fcaR documentation built on July 27, 2026, 5:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| FormalContext | R Documentation |
R6 class for a formal context
Description
This class implements the data structure and methods for formal contexts.
Public fields
IThe table of the formal context as a matrix.
attributesThe attributes of the formal context.
objectsThe objects of the formal context.
grades_setThe set of degrees (in [0, 1]) the whole set of attributes can take.
expanded_grades_setThe set of degrees (in [0, 1]) each attribute can take.
conceptsThe concept lattice associated to the formal context as a
ConceptLattice.implicationsA set of implications on the formal context as an
ImplicationSet.descriptionAn optional description of the dataset
Methods
Public methods
Method new()
Creator for the Formal Context class
Usage
FormalContext$new(I, filename, remove_const = FALSE)
Arguments
I(numeric matrix) The table of the formal context.
filename(character) Path of a file to import.
remove_const(logical) If
TRUE, remove constant columns. The default isFALSE.
Details
Columns of I should be named, since they are the names of the attributes of the formal context.
If no I is used, the resulting FormalContext will be empty and not usable unless for loading a previously saved one. In this case, one can provide a filename to import. Only RDS, CSV and CXT files are currently supported.
If the file is not present, the fcarepository.org is looked for coincidences. If so, the corresponding context is loaded.
Returns
An object of the FormalContext class.
Method is_empty()
Check if the FormalContext is empty
Usage
FormalContext$is_empty()
Returns
TRUE if the FormalContext is empty, that is, has not been provided with a matrix, and FALSE otherwise.
Method scale()
Scale the context
Usage
FormalContext$scale(attributes, type, ...)
Arguments
attributesThe attributes to scale
typeType of scaling.
...
Details
The types of scaling are implemented in a registry,
so that scalingRegistry$get_entries() returns
all types.
In the dots argument, the user can supply the value for bg (logical), which, if set to TRUE, indicates to compute background knowledge as implications on the scales; if FALSE, no implications will be computed on the scales.
Returns
The scaled formal context
Examples
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
Method get_scales()
Scales applied to the formal context
Usage
FormalContext$get_scales(attributes = names(private$scales))
Arguments
attributes(character) Name of the attributes for which scales (if applied) are returned.
Returns
The scales that have been applied to the specified attributes
of the formal context. If no attributes are passed,
then all applied scales are returned.
Examples
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$get_scales()
Method background_knowledge()
Background knowledge of a scaled formal context
Usage
FormalContext$background_knowledge()
Returns
An ImplicationSet with the implications
extracted from the application of scales.
Examples
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$background_knowledge()
Method dual()
Get the dual formal context
Usage
FormalContext$dual()
Returns
A FormalContext where objects and attributes have interchanged their roles.
Method intent()
Get the intent of a fuzzy set of objects
Usage
FormalContext$intent(...)
Arguments
...A
Setof objects, or object names.
Returns
A Set with the intent.
Method uparrow()
Get the intent of a fuzzy set of objects
Usage
FormalContext$uparrow(S)
Arguments
S(
Set) The set of objects to compute the intent for.
Returns
A Set with the intent.
Method extent()
Get the extent of a fuzzy set of attributes
Usage
FormalContext$extent(...)
Arguments
...A
Setof attributes, or attribute names.
Returns
A Set with the intent.
Method downarrow()
Get the extent of a fuzzy set of attributes
Usage
FormalContext$downarrow(S)
Arguments
S(
Set) The set of attributes to compute the extent for.
Returns
A Set with the intent.
Method closure()
Get the closure of a fuzzy set of attributes
Usage
FormalContext$closure(...)
Arguments
...A
Setof attributes, or attribute names.
Returns
A Set with the closure.
Method obj_concept()
Object Concept
Usage
FormalContext$obj_concept(object)
Arguments
object(character) Name of the object to compute its associated concept
Returns
The object concept associated to the object given.
Method att_concept()
Attribute Concept
Usage
FormalContext$att_concept(attribute)
Arguments
attribute(character) Name of the attribute to compute its associated concept
Returns
The attribute concept associated to the attribute given.
Method is_concept()
Is a Concept?
Usage
FormalContext$is_concept(C)
Arguments
CA
Conceptobject
Returns
TRUE if C is a concept.
Method is_closed()
Testing closure of attribute sets
Usage
FormalContext$is_closed(...)
Arguments
...A
Setof attributes, or attribute names.
Returns
TRUE if the set S is closed in this formal context.
Method clarify()
Clarify a formal context
Usage
FormalContext$clarify(copy = FALSE)
Arguments
copy(logical) If
TRUE, a newFormalContextobject is created with the clarified context, otherwise the current one is overwritten.
Returns
The clarified FormalContext.
Method reduce()
Reduce a formal context
Usage
FormalContext$reduce(copy = FALSE, method = c("arrows", "concepts"))Arguments
copy(logical) If
TRUE, a newFormalContextobject is created with the clarified and reduced context, otherwise the current one is overwritten.method(character) The method to use for reduction. One of
"arrows"(default, uses arrow relations, only for binary contexts) or"concepts"(uses irreducible concepts, works for fuzzy).
Returns
The clarified and reduced FormalContext.
Method standardize()
Build the Standard Context
Usage
FormalContext$standardize()
Details
This is a wrapper around reduce(copy = TRUE, method = "arrows") for binary contexts,
or reduce(copy = TRUE, method = "concepts") for fuzzy contexts.
Returns
The standard context using the join- and meet- irreducible elements.
Method find_concepts()
Use Ganter Algorithm to compute concepts
Usage
FormalContext$find_concepts(method = "InClose", verbose = FALSE)
Arguments
method(string) The name of a method for the computation of concepts. Available options can be listed with
conceptRegistry$get_entries().verbose(logical) TRUE will provide a verbose output.
Returns
A list with all the concepts in the formal context.
Method find_implications()
Use modified Ganter algorithm to compute both concepts and implications
Usage
FormalContext$find_implications(
save_concepts = TRUE,
verbose = FALSE,
method = c("LinCbO", "NextClosure")
)Arguments
save_concepts(logical)
TRUEwill also compute and save the concept lattice.FALSEis usually faster, since it only computes implications.verbose(logical)
TRUEwill provide a verbose output.method(character) Algorithm to use for binary contexts. One of
"LinCbO"(default, Janostik, Konecny, Krajca) or"NextClosure"(Ganter's algorithm). For non-binary (fuzzy) contexts, NextClosure is always used.
Returns
Nothing, just updates the internal fields concepts and implications.
Method find_protoconcepts()
Find protoconcepts
Usage
FormalContext$find_protoconcepts(verbose = FALSE)
Arguments
verbose(logical) Show verbose output.
Returns
A list of protoconcepts, where each protoconcept is a list of two Sets (extent and intent).
Method find_causal_rules()
Find causal rules
Usage
FormalContext$find_causal_rules( response_var, min_support = 0.1, confidence_level = 0.95, max_length = 3, verbose = FALSE )
Arguments
response_var(character) The name of the response variable.
min_support(numeric) Minimum support for the premise attributes.
confidence_level(numeric) Confidence level for the causality test.
max_length(integer) Maximum length of the premise.
verbose(logical) Show verbose output.
Returns
A RuleSet object containing the discovered causal rules and their quality metrics.
Method factorize()
Factorize the formal context using Boolean Matrix Factorization (BMF) algorithms. Note: Fuzzy contexts are currently not supported and will result in an error.
Usage
FormalContext$factorize(method = "GreConD", ...)
Arguments
method(character) The algorithm to use. Supported algorithms: "RSF", "RSF-ES", "GreConD", "GreEss", "ASSO", "PaNDa+ MDL", "PaNDa+ ASSO-style", "PaNDa+ Weighted", "Hyper", "Hyper+".
...Additional arguments depending on the method:
-
GreConD,PaNDa+ *,ASSO:k(integer) maximum factors to extract. -
ASSO:threshold(default 0.6),w_pos(default 1.0),w_neg(default 1.0). -
PaNDa+ Weighted:rho(default 1.0). -
Hyper,Hyper+:min_support(ratio, default 0.05). -
Hyper+:beta(default 0.1).
-
Returns
A list with two FormalContext objects:
-
object_factor: The context mapping Objects to Factors (Matrix A / U). -
factor_attribute: The context mapping Factors to Attributes (Matrix B / V).
Method to_transactions()
Convert the formal context to object of class transactions from the arules package
Usage
FormalContext$to_transactions()
Returns
A transactions object.
Method save()
Save a FormalContext to RDS or CXT format
Usage
FormalContext$save(filename = tempfile(fileext = ".rds"))
Arguments
filename(character) Path of the file where to store the
FormalContext.
Details
The format is inferred from the extension of the filename.
Returns
Invisibly the current FormalContext.
Method load()
Load a FormalContext from a file
Usage
FormalContext$load(filename)
Arguments
filename(character) Path of the file to load the
FormalContextfrom.
Details
Currently, only RDS, CSV and CXT files are supported.
Returns
The loaded FormalContext.
Method dim()
Dimensions of the formal context
Usage
FormalContext$dim()
Returns
A vector with (number of objects, number of attributes).
Method to_json()
Export the formal context to JSON
Usage
FormalContext$to_json(file = NULL)
Arguments
file(character) The path of the file to save the JSON to.
Returns
A JSON string representing the formal context.
Method print()
Prints the formal context
Usage
FormalContext$print()
Returns
Prints information regarding the formal context.
Method calculate_arrow_relations()
Calculate arrow relations for a binary context.
Usage
FormalContext$calculate_arrow_relations()
Details
This method computes the arrow relations (swarrow, nearrow, and double arrow).
Returns
Nothing, updates the internal state.
Method get_arrow_relations()
Get arrow relations
Usage
FormalContext$get_arrow_relations()
Returns
An integer matrix with the arrow relations.
Method get_irreducible_objects()
Get irreducible objects
Usage
FormalContext$get_irreducible_objects()
Returns
A character vector with the names of the irreducible objects.
Method get_irreducible_attributes()
Get irreducible attributes
Usage
FormalContext$get_irreducible_attributes()
Returns
A character vector with the names of the irreducible attributes.
Method is_distributive()
Check if the context (and its lattice) is distributive
Usage
FormalContext$is_distributive()
Returns
Logical: TRUE if the lattice is distributive.
Method reduce_arrows()
Reduce the formal context using arrow relations
Usage
FormalContext$reduce_arrows()
Returns
A new FormalContext object that is clarified and reduced.
Method get_core()
Get the Core of the Formal Context
Usage
FormalContext$get_core()
Details
The core is the minimal subcontext that generates the same concept lattice.
For binary contexts, this is equivalent to standardize().
Returns
A new FormalContext object containing only irreducible rows and columns.
Method to_latex()
Write the context in LaTeX format
Usage
FormalContext$to_latex(table = TRUE, label = "", caption = "")
Arguments
table(logical) If
TRUE, surrounds everything between\begin{table}and\end{table}.label(character) The label for the table environment.
caption(character) The caption of the table.
fraction(character) If
none, no fractions are produced. Otherwise, if it isfrac,dfracorsfrac, decimal numbers are represented as fractions with the corresponding LaTeX typesetting.
Returns
A table environment in LaTeX.
Method incidence()
Incidence matrix of the formal context
Usage
FormalContext$incidence()
Returns
The incidence matrix of the formal context
Examples
fc <- FormalContext$new(planets) fc$incidence()
Method subcontext()
Generates a new FormalContext restricted to a subset of objects and/or attributes.
Usage
FormalContext$subcontext(objects, attributes)
Arguments
objects(character or integer vector) The names or indices of the objects to keep. If NULL, keeps all.
attributes(character or integer vector) The names or indices of the attributes to keep. If NULL, keeps all.
Returns
A new FormalContext object representing the subcontext.
Examples
fc <- FormalContext$new(planets)
fc$subcontext(attributes = c("moon", "no_moon"))
Method [()
Subcontext of the formal context
Usage
FormalContext$[(objects, attributes)
Arguments
objects(character array) Name of the objects to keep.
attributes(character array) Names of the attributes to keep.
Details
A warning will be issued if any of the names is not present in the list of objects or attributes of the formal context.
If objects or attributes is empty, then it is
assumed to represent the whole set of objects or attributes
of the original formal context.
Returns
Another FormalContext that is a subcontext
of the original one, with only the objects and attributes
selected.
Examples
fc <- FormalContext$new(planets)
fc[, c("moon", "no_moon")]
Method plot()
Plot the formal context table
Usage
FormalContext$plot(to_latex = FALSE, ...)
Arguments
to_latex(logical) If
TRUE, export the plot as atikzpictureenvironment that can be included in aLaTeXfile....Other parameters to be passed to the
tikzDevicethat renders the lattice inLaTeX, or for the figure caption. SeeDetails.
Details
Particular parameters that control the size of the tikz output are: width, height (both in inches), and pointsize (in points), that should be set to the font size used in the documentclass header in the LaTeX file where the code is to be inserted.
If a caption is provided, the whole tikz picture will be wrapped by a figure environment and the caption set.
Returns
If to_latex is FALSE, it returns nothing, just plots the graph of the formal context. Otherwise, this function returns the LaTeX code to reproduce the formal context plot.
Method use_logic()
Sets the logic to use
Usage
FormalContext$use_logic(name = available_logics())
Arguments
nameThe name of the logic to use. To see the available names, run
available_logics().
Method get_logic()
Gets the logic used
Usage
FormalContext$get_logic()
Returns
A string with the name of the logic.
Method use_connection()
Sets the name of the Galois connection to use
Usage
FormalContext$use_connection(connection)
Arguments
connectionThe name of the Galois connection. Available connections are "standard" (antitone), "benevolent1" and "benevolent2" (isotone)
Method get_connection()
Gets the name of the Galois connection
Usage
FormalContext$get_connection()
Returns
A string with the name of the Galois connection
Method clone()
The objects of this class are cloneable with this method.
Usage
FormalContext$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
References
Guigues J, Duquenne V (1986). “Familles minimales d'implications informatives résultant d'un tableau de données binaires.” Mathématiques et Sciences humaines, 95, 5-18.
Ganter B, Wille R (1999). Formal concept analysis : mathematical foundations. Springer. ISBN 3540627715.
Belohlavek R (2002). “Algorithms for fuzzy concept lattices.” In Proc. Fourth Int. Conf. on Recent Advances in Soft Computing. Nottingham, United Kingdom, 200-205.
Hahsler M, Grun B, Hornik K (2005). “arules - a computational environment for mining association rules and frequent item sets.” J Stat Softw, 14, 1-25.
Examples
# Build and print the formal context
fc_planets <- FormalContext$new(planets)
print(fc_planets)
# Define a set of attributes
S <- Set$new(attributes = fc_planets$attributes)
S$assign(moon = 1, large = 1)
# Compute the closure of S
Sc <- fc_planets$closure(S)
# Is Sc a closed set?
fc_planets$is_closed(Sc)
# Clarify and reduce the formal context
fc2 <- fc_planets$reduce(TRUE)
# Find implications
fc_planets$find_implications()
# Read a formal context from CSV
filename <- system.file("contexts", "airlines.csv", package = "fcaR")
fc <- FormalContext$new(filename)
# Read a formal context from a CXT file
filename <- system.file("contexts", "lives_in_water.cxt", package = "fcaR")
fc <- FormalContext$new(filename)
## ------------------------------------------------
## Method `FormalContext$scale`
## ------------------------------------------------
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
## ------------------------------------------------
## Method `FormalContext$get_scales`
## ------------------------------------------------
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$get_scales()
## ------------------------------------------------
## Method `FormalContext$background_knowledge`
## ------------------------------------------------
filename <- system.file("contexts", "aromatic.csv", package = "fcaR")
fc <- FormalContext$new(filename)
fc$scale("nitro", "ordinal", comparison = `>=`, values = 1:3)
fc$scale("OS", "nominal", c("O", "S"))
fc$scale(attributes = "ring", type = "nominal")
fc$background_knowledge()
## ------------------------------------------------
## Method `FormalContext$incidence`
## ------------------------------------------------
fc <- FormalContext$new(planets)
fc$incidence()
## ------------------------------------------------
## Method `FormalContext$subcontext`
## ------------------------------------------------
fc <- FormalContext$new(planets)
fc$subcontext(attributes = c("moon", "no_moon"))
## ------------------------------------------------
## Method `FormalContext$[`
## ------------------------------------------------
fc <- FormalContext$new(planets)
fc[, c("moon", "no_moon")]
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.