fitness: Regional Fitness and/or Technological Complexity

In n3ssuno/RKS: Regional Knowledge Space

Description

The function computes the fitness index of each region (i.e. its competitiveness), in line with the methodology proposed by Pietronero and coauthors.

Usage

fitness(occt, rta = TRUE, binary = TRUE, which = "fitness")

Arguments

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.
rta	If TRUE (default) it uses the Revealed Technological Advantages (RTA) of the original data
binary	If TRUE (default) it dichotomize the RTA matrix (can be used only together with rta=TRUE).
which	It can be one of "fitness" (default), "complexity", "both". The first returns the fitness of each region; the second returns the complexity of each technological domain; and the third returns both the indices.

Value

A data.frame with the Fitness of each region and/or the Complexity of each technological domain. If a 3D array is provided as input, it returns the full panel data.frame.

References

Tacchella, Cristelli, Caldarelli, Gabrielli and Pietronero (2012) “A New Metrics for Countries' Fitness and Products' Complexity”, Scientific Reports, 2, 1–7;

Tacchella, Cristelli, Caldarelli, Gabrielli and Pietronero (2013) “Economic Complexity: Conceptual Grounding of a New Metrics for Global Competitiveness”, Journal of Economic Dynamics and Control, 37, 1683–1691;

Cristelli, Gabrielli, Tacchella, Caldarelli and Pietronero (2013) “Measuring the Intangibles: A Metrics for the Economic Complexity of Countries and Products”, PLoS ONE, 8, e70726;

Examples

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
FX <- fitness(octab)
attr(FX, "convergence")

n3ssuno/RKS documentation built on Jan. 15, 2020, 5:15 p.m.