| z_score {EconGeo} | R Documentation |
Compute the z-score between technologies from an incidence matrix
Description
This function computes the z-score between pairs of technologies from a patent-technology incidence matrix. The z-score is a measure to analyze the co-occurrence of technologies in patent documents (i.e. knowledge combination). It compares the observed number of co-occurrences to what would be expected under the hypothesis that combination is random. A positive z-score indicates a typical co-occurrence which has occurred multiple times before. In contrast, a negative z-socre indicates an atypical co-occurrence. The z-score has been used to estimate the degree of novelty of patents (Kim 2016), scientific publications (Uzzi et al. 2013) or the relatedness between industries (Teece et al. 1994).
Usage
z_score(mat)
Arguments
mat |
A patent-technology incidence matrix with patents in rows and technologies in columns |
Value
A matrix of z-scores representing the co-occurrence of technologies in the input incidence matrix. The z-score measures the deviation of the observed co-occurrence from the expected co-occurrence under the assumption of random combination. Positive z-scores indicate typical co-occurrences, while negative z-scores indicate atypical co-occurrences.
Author(s)
Lars Mewes mewes@wigeo.uni-hannover.de
References
Kim, D., Cerigo, D. B., Jeong, H., and Youn, H. (2016). Technological novelty proile and invention's future impact. EPJ Data Science, 5 (1):1–15
Teece, D. J., Rumelt, R., Dosi, G., and Winter, S. (1994). Understanding corporate coherence. Theory and evidence. Journal of Economic Behavior and Organization, 23 (1):1–30
Uzzi, B., Mukherjee, S., Stringer, M., and Jones, B. (2013). Atypical Combinations and Scientific Impact. Science, 342 (6157):468–472
See Also
relatedness_density, co_occurrence
Examples
## Generate a toy incidence matrix
set.seed(2210)
techs <- paste0("T", seq(1, 5))
techs <- sample(techs, 50, replace = TRUE)
patents <- paste0("P", seq(1, 20))
patents <- sort(sample(patents, 50, replace = TRUE))
my_data <- data.frame(patents, techs)
my_dat <- unique(my_data)
mat <- as.matrix(table(my_data$patents, my_data$techs))
## run the function
z_score(mat)