seatsVotes               package:pscl               R Documentation

_A _c_l_a_s_s _f_o_r _c_r_e_a_t_i_n_g _s_e_a_t_s-_v_o_t_e_s _c_u_r_v_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Convert a vector of vote shares into a seats-vote curve object,
     providing estimates of partisan bias.

_U_s_a_g_e:

     seatsVotes(x, desc = NULL, method = "uniformSwing")

_A_r_g_u_m_e_n_t_s:

       x: a vector of vote shares for a specific party (either
          proportions or percentages)

    desc: descriptive text

  method: how to simulate a seats-vote curve; the only supported method
          at this stage is 'uniformSwing'.

_D_e_t_a_i_l_s:

     Simulation methods are required to induce a seats-votes curve
     given a vector of vote shares from one election.  The uniform
     swing method simply slides the empirical distribution function of
     the vote shares "up" and "down", computing the proportion of the
     vote shares that lie above .5 (by construction, the winning
     percentage in a two-party election) for each new location of the
     vector of vote shares.  That is, as the empirical CDF of the
     observed vote shares slides up or down, more or less seats cross
     the .5 threshold.  A seats-votes curve is formed by plotting the
     seat share above .5 as a function of the average district-level
     vote share (a weakly monotone function, since the empirical CDF
     constitutes a set of sufficient statistics for this problem).  The
     simulation is run so as to ensure that average district-level vote
     shares range between 0 and 1.

     The extent to which the seats-votes curve departs from symmetry is
     known as bias.  More specifically, the vertical displacement of
     the seats-votes curve from .5 when average district-level vote
     share is .5 is conventionally reported as an estimate of the bias
     of the electoral system.  

     Different methods produce different estimates of seats-votes
     curves and summary estimands such as bias.  The uniform swing
     method is completely deterministic and does not produce any
     uncertainty assessment (e.g., confidence intervals etc).

_V_a_l_u_e:

     An object of class 'seatsVotes', with components 

       s: Estimated seat shares over the range of simulated average,
          district-level vote shares

       v: Simulated average district-level vote shares

       x: observed seat shares, with missing data removed

    desc: user-supplied descriptive character string

    call: a list of class 'call', the call to the function

_N_o_t_e:

     Additional methods to come later.

_A_u_t_h_o_r(_s):

     Simon Jackman jackman@stanford.edu

_R_e_f_e_r_e_n_c_e_s:

     Tufte, Edward R. 1973. The Relationship Between Seats and Votes in
     Two-Party Systems. _American Political Science Review_.
     67(2):540-554.

     Gelman, Andrew and Gary King.  1990.  Estimating the Consequences
     of Electoral Redistrictring.  _Journal of the American Statistical
     Association_. 85:274-282.

     Jackman, Simon. 1994.  Measuring Electoral Bias: Australia,
     1949-93. _British Journal of Political Science_. 24(3):319-357.

_S_e_e _A_l_s_o:

     'plot.seatsVotes' for plotting methods.

_E_x_a_m_p_l_e_s:

     data(ca2006)
     x <- ca2006$D/(ca2006$D+ca2006$R)
     sv <- seatsVotes(x,
                      desc="Democratic Vote Shares, California 2006 congressional elections")

