coherence: Coherence
In n3ssuno/RKS: Regional Knowledge Space

Description Usage Arguments Details Value References Examples

View source: R/coherence.R

The function computes the so called Coherence index.

1	coherence(occt, relatedness_mtx)

occt

Contingency table (i.e., occurrence table or incidence matrix) on which you want to compute the indices. It can be a 2D array, in which the first dimension represents the units of analysis (like firms, regions, or countries), and the second dimension represents the events or characteristics of interest (like the classes of the patents produced by the regions, or the sectors in which the workers belongs). Lastly, the values in each cell represents the occurrences of each unit-event pair. Moreover, you can use also a 3D array if you like, in which the third dimension represents the time. The object is expected to be of "table" class.

relatedness_mtx

Matrix of similarity between the technological classes. The ReKS package provides some function to build it: see relatedness and proximity.

The function computes the so called Coherence index. It assumes that the "universe" from which you derive the distribution of reference is composed by all the information provided in the database, and only this. You can use it both on a panel data set (if you identify also a column with a temporal indication of the observations) or on cross-section data (by leaving the parameter just said as NULL).

A data.frame with the Coherence Index of each geographical area.

Teece, Rumelt, Dosi and Winter (1994) “Understanding Corporate Coherence: Theory and Evidence”, Journal of Economic Behavior & Organization, 23, 1–30;

Nesta and Saviotti (2005) “Coherence of the Knowledge Base and the Firm's Innovative Performance: Evidence from the U.S.~Pharmaceutical Industry”, Journal of Industrial Economics, 53, 123–142;

Nesta and Saviotti (2006) “Firm Knowledge and Market Value in Biotechnology”, Industrial and Corporate Change, 15, 625–652;

Nesta (2008) “Knowledge and Productivity in the World's Largest Manufacturing Corporations”, Journal of Economic Behavior \& Organization, 67, 886–902,

Bottazzi and Pirino (2010) “Measuring Industry Relatedness and Corporate Coherence”, SSRN Electronic Journal, 11, 1–24;

Quatraro (2010) “Knowledge Coherence, Variety and Economic Growth: Manufacturing Evidence from Italian Regions”, Research Policy, 39, 1289-1302.

geo <- paste0("R", 10:50)
tech <- paste0("T", 10:90)
time <- 1:5
dat <- expand.grid(geo, tech, time)
colnames(dat) <- c("geo", "tech", "time")
set.seed(1)
dat$nPat <- sample(1:200, nrow(dat), replace = TRUE)
octab <- xtabs(nPat ~ geo + tech + time, dat)
octab[sample(1:length(octab), length(octab)/2)] <- 0
rel_m <- relatedness(octab, output_statistic = "p")
RCH <- coherence(octab, rel_m)