vfs: Votes from Seats Laws

In philswatton/polimetrics: Miscellaneous functions useful for political science

Votes from Seats Laws

Description

Implementations of the four laws from the book Votes from Seats.

Usage

vfs(S, M, t = NULL, ret_SM = T)

Ns(S, M, t = NULL)

s1(S, M, t = NULL)

Nv(S, M, t = NULL)

v1(S, M, t = NULL)

Arguments

S	Vector of assembly sizes. If t is provided, the vector of basic tier assembly sizes.
M	Vector of average district sizes. If t is provided, the vector of basic tier average district sizes.
t	Optional, vector of upper tier seat shares.
ret_SM	A TRUE or FALSE value denoting whether or not to return the values of S and M, and if supplied, t

Details

These functions implement the four fundamental laws from the book Votes from Seats in R.

These laws are:

Number of effective parties in terms of seats:

Ns = (MS)^(1/6)

Seat share of the largest party:

s1 = (MS)^(-1/8)

Number of effective parties in terms of votes:

Nv = ((MS)^(1/4)+1)^(2/3)

Vote share of the largest party:

v1 = ((MS)^(1/4)+1)^(-1/2)

If t is supplied, then these are instead calculated as:

Number of effective parties in terms of seats:

Ns = (2.5^t)*(MS)^(1/6)

Seat share of the largest party:

s1 = (0.5^t)*(MS)^(1/8)

Number of effective parties in terms of votes:

Nv = ((4^t)*((MS)^(1/4)) + 1)^(2/3)

Vote share of the largest party:

v1 = ((4^t)*((MS)^(1/4)) + 1)^(-1/2)

Value

Ns, s1, Nv, v1 all return a vector of numeric values. vfs returns a dataframe containing six columns, S, M, and a column for each of the measures.

References

\insertRef

shugart2017psmisc

philswatton/polimetrics documentation built on Jan. 30, 2023, 3:21 p.m.