Calculate individual representation scores for categorical data.
This function calculates individual representation scores based on frequency vectors. If the data are in this form, this function is much faster than the standard IRS function.
irs.cat(G, H, perspective = "individual")
frequency vector for the population
frequency vector for the representatives
individual perspective (default) or population perspective
This function calculates individual representation scores. You will need two frequency vectors, one for the population, and one for the representatives. The two frequency vectors need to be of the same length. It is possible to choose the perspective of the population, which leads to slightly different individual representation scores for small N.
A vector of the same length as the frequency vector for the population. For each position, the individual representation scores are given.
Ruedin, D. (2012) Individual representation: A different approach to political representation. Representation 48(1): 115-29.
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# Sample data G <- c(1,5,10,15,3) H <- c(0,3,0,5,1) # Calculate individual representation scores irs.cat(G,H) # The representation scores for each position are: # 5.65 2.05 -1.70 -2.95 3.75 # We have small N, so let's look at the population perspective irs.cat(G, H, perspective = "population") # The representation scores for each position are now: # 4.2 1.0 -3.0 -5.0 2.6
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