bibliometrix (version 1.8)

conceptualStructure: Creating and plotting conceptual structure map of a scientific field

Description

The function conceptualStructure creates a conceptual structure map of a scientific field performing Multiple Correspondence Analysis (MCA) and Clustering of a bipartite network of terms extracted from keyword, title or abstract fields.

Usage

conceptualStructure(M, field = "ID", quali.supp = NULL,
  quanti.supp = NULL, minDegree = 2, k.max = 5, stemming = FALSE,
  labelsize = 3)

Arguments

M

is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original ISI or SCOPUS file.

field

is a character object. It indicates one of the field tags of the standard ISI WoS Field Tag codify. field can be equal to one of this tags:

ID		Keywords Plus associated by ISI or SCOPUS database
DE		Author's keywords
ID_TM		Keywords Plus stemmed through the Porter's stemming algorithm
DE_TM		Author's Keywords stemmed through the Porter's stemming algorithm
TI		Terms extracted from titles

quali.supp

is a vector indicating the indexes of the categorical supplementary variables.

quanti.supp

is a vector indicating the indexes of the quantitative supplementary variables.

minDegree

is an integer. It indicates the minimun occurrences of terms to analize and plot. The default value is 2.

k.max

is an integer. It indicates the maximum numebr of cluster to keep. The default value is 5. The max value is 8.

stemming

is logical. If TRUE the Porter's Stemming algorithm is applied to all extracted terms. The default is stemming = FALSE.

labelsize

is an integer. It indicates the label size in the plot. Default is labelsize=2

Value

It is an object of the class list containing the following components:

net		bipartite network
res.mca		Results of Multiple Correspondence Analysis

See Also

termExtraction to extract terms from a textual field (abstract, title, author's keywords, etc.) of a bibliographic data frame.

biblioNetwork to compute a bibliographic network.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# NOT RUN {
# EXAMPLE Conceptual Structure using Keywords Plus

data(scientometrics)

S <- conceptualStructure(scientometrics, field="ID_TM", stemming=TRUE, minDegree=5, k.max = 5)

# }