UpperIndexBound {robustrao} | R Documentation |
Upper bound of the uncertainty interval of the Rao-Stirling diversity index.
Description
This function computes the upper bound of the uncertainty interval of the Rao-Stirling diversity index, as explained in Calatrava et al. (2016). The computation involves the redistribution of uncategorized references to various disciplines. In order to avoid improbable redistributions of disciplines, a set of permissible disciplines for redistribution can be defined. Furthermore, the number of disciplines redistributed to uncategorized references can be limited.
Usage
UpperIndexBound(known.ref.counts, uncat.ref.count, similarity,
permissible.disciplines = NULL, redistribution.limit = 4)
Arguments
known.ref.counts |
A vector of positive integers. Each element represents the count of references to each discipline. |
uncat.ref.count |
A positive integer denoting the number of references that are not categorized into disciplines. |
similarity |
A positive semi-definite matrix that encodes the similarity between disciplines, as explained in Porter and Rafols (2009). The dimensions of this matrix are n x n, being n the total number of disciplines. The self-similarities (i.e. the diagonal elements) have to be 1. |
permissible.disciplines |
A logical vector denoting to which disciplines uncategorized references can be distributed.
Its length needs to be equal to the length of |
redistribution.limit |
A positive integer that limits the number of disciplines that each uncategorized reference can have redistributed. This argument is optional and leaving it unspecified will set the redistribution.limit to default. |
Value
The upper bound of the uncertainty interval of the Rao-Stirling diversity index.
References
Calatrava Moreno, M.C., Auzinger, T. and Werthner, H. (2016) On the uncertainty of interdisciplinarity measurements due to incomplete bibliographic data. Scientometrics. DOI:10.1007/s11192-016-1842-4
Porter, A. and Rafols, I. (2009) Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, Vol. 81, No. 3 (719-745). DOI:10.1007/s11192-008-2197-2
Examples
#Load data
data(pubdata1)
#Get counts of citations of one of the publications in the dataset
counts <- pd1.count.matrix[,1]
#Get number of uncategorized references in the publication
uncat <- pd1.uncat.refs[1]
#Get vector of permissible disciplines.
logic.disciplines <- counts > 0
permissible <- PruneDisciplines(logic.disciplines, 0.233, pd1.similarity)
UpperIndexBound(counts, uncat, pd1.similarity, permissible)