Performance-class: Class '"Performance"'

In mpia: Meaningful Purposive Interaction Analysis

Description

The Performance holds a digital record representing a particular human action: it stores a representation of meaning and an human-readable label for the purpose of the action. The purpose thereby is a character string, whereas the meaning vector is constructed by mapping the descriptive source text to its social semantic vector space representation.

Several class methods (and generics) support in inspecting the result of such mapping: terms, for example, returns all terms activated above threshold in the vector space through the fold in of the sourcetext.

Each Performance object is bound to a particular Domain.

Fields

name:

Character string holding the human-readable label.

sourcetext:

Character string holding the original source text.

logging:

Logical flag indicating whether to internally store the source text (or discard it, once the meaning vector is constructed).

meaningvector:

Index position of the meaning vector in the Domain's traces matrix.

terms:

The top loading terms (above threshold).

domain:

Reference pointing to the according Domain object.

purpose:

Character string indicating the intended purpose of the performance.

Generics

+

Add together two meaning vectors, return result vector.

==

Test two performances for identity (i.e. whether they are close to each other above the proximity threshold).

competences

Calculate the competence positions underlying the vector with references to performances. In case this is only one performance, it returns the performance.

cosine

Calculate the cosine between performances.

names

Return character string with the human-readable label of the performance record.

names<-

Set the human-readable label of the performance record.

near

Return cosine closeness value of performances (or performances and persons) tested.

overlap

Return a vector containing those terms that are shared by all performance objects under investigation.

plot

Plot a marker indicating the location of the performance on the projection surface created by the map visualisation of the domain.

position

Return the competence position held by a vector of performances (calculated as the centroid of the performances).

print

Pretty printing the performance object.

proximity

Return TRUE if the performances in the argument(s) are in proximity to each other (above the proximityThreshold of the domain).

show

Display the object by printing its key characteristics.

summary

Describe the performance (top loading terms, name, source text).

terms

Return a vector of character strings containing the top loading terms of the performance (activated above threshold).

Class Methods

initialize(text, purpose, domain, name, weighting, logging):

Constructor: requires the sourcetext text, can optionally set purpose and name. Logging indicates whether the sourcetext will be stored internally. A weighting function can be handed over.

getName():

Get the name of the performance (better use the generic names instead).

setName(value):

Set the name of the performance (better use the generic names<- instead).

getSourceText():

Return a character string with the raw source text of the performance.

setMeaningVector(vec):

Add the meaning vector to the domain's trace matrix field and internally store the index position in field meaningvector.

getActivatedTerms(threshold):

Return a vector of character strings containing the top loading terms of the performance (activated above threshold). Use the generic terms instead.

getMeaningVector():

Fetch the meaning vector from the domain's traces matrix and return it.

getDomain():

Return reference pointing to the according domain object.

getPurpose():

Return character string with the human-readable purpose the performance aimed at achieving.

print():

Pretty printing the performance object (use the generic print instead).

show():

Display the object by printing its key characteristics (use the generic show instead).

Author(s)

Fridolin Wild <[e-mail]>

References

Fridolin Wild (2016): Learning Analytics in R with SNA, LSA, and MPIA, Springer:Berlin.

See Also

terms, overlap, near, proximity, competences, names, position, plot, Domain-class, Domain

Examples

d = Domain(name="test")
evidence = TermDocumentMatrix( Corpus( VectorSource( c("abc abc def",
    "def ghi", "ghi abc, lmno", "lmno abc wxyz") ) ) )
d$corpus(evidence)
d$spacify()

p = Performance(text="abc def ghi", domain=d, purpose="demo", name="demo")
terms(p)

mpia documentation built on May 2, 2019, 4:18 p.m.