irs.plot: Plot individual representation scores for categorical data.

In polrep: Calculate Political Representation Scores

Plot individual representation scores for categorical data.

Description

Plots individual representation scores based on frequency vectors.

Usage

irs.plot(G, H, perspective = "individual", groups=c("Voters","MP"), concept="Ideology")

Arguments

G	frequency vector for the population
H	frequency vector for the representatives
perspective	individual perspective (default) or population perspective
groups	label for the frequency vectors
concept	label for the concept measures

Details

This function plots individual representation scores for categorical data. As with the underlying irs.cat, 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. Use the argument groups to specify what these frequency capture. The default labels are ‘Voters’ and ‘MP’. You can also specify the concept measured, such as left-right or social policies (default label: ‘ideology’. It is further possible to choose the perspective of the population, which leads to slightly different individual representation scores for small N.

The plots show the frequency distributions of the two vectors supplied at the top, and the resulting individual representation scores for each category.

Author(s)

Didier Ruedin

References

Ruedin, D. (2012) Individual representation: A different approach to political representation. Representation 48(1): 115-29.

See Also

irs.cat

Examples

# Sample data
G <- c(1,5,10,15,3)
H <- c(0,3,0,5,1)
# Calculate individual representation scores
irs.cat(G,H)
# Plot this:
irs.plot(G,H)

polrep documentation built on Jan. 5, 2024, 3:01 a.m.