dtomogplot             package:MCMCpack             R Documentation

_D_y_n_a_m_i_c _T_o_m_o_g_r_a_p_h_y _P_l_o_t

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

     dtomogplot is used to produce a tomography plot (see King, 1997)
     for a series of temporally ordered, partially observed 2 x 2
     contingency tables.

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

     dtomogplot(r0, r1, c0, c1, time.vec=NA, delay=0,
                xlab="fraction of r0 in c0 (p0)",
                ylab="fraction of r1 in c0 (p1)",
                color.palette=heat.colors, bgcol="black", ...)

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

      r0: An (ntables * 1) vector of row sums from row 0.

      r1: An (ntables * 1) vector of row sums from row 1.

      c0: An (ntables * 1) vector of column sums from column 0.

      c1: An (ntables * 1) vector of column sums from column 1.

time.vec: Vector of time periods that correspond to the elements of r0,
          r1, c0, and c1.

   delay: Time delay in seconds between the plotting of the tomography
          lines. Setting a positive delay is useful for visualizing
          temporal dependence.

    xlab: The x axis label for the plot.

    ylab: The y axis label for the plot.

color.palette: Color palette to be used to encode temporal patterns.

   bgcol: The background color for the plot.

     ...: further arguments to be passed

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

     Consider the following partially observed 2 by 2 contingency
     table:


                  | Y=0      | Y=1      |
       - - - - -  - - - - -  - - - - -  - - - - -
       X=0        | Y0       |          | r0
       - - - - -  - - - - -  - - - - -  - - - - -
       X=1        | Y1       |          | r1
       - - - - -  - - - - -  - - - - -  - - - - -
                  | c0       | c1       | N

     where r0, r1, c0, c1, and N  are non-negative integers that are
     observed. The interior cell entries are not observed. It is
     assumed that Y0|r0 ~ Binomial(r0, p0) and Y1|r1 ~ Binomial(r1,p1).

     This function plots the bounds on the maximum likelihood estimates
     for (p0, p1) and color codes them by the elements of time.vec.

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

     Gary King, 1997. _A Solution to the Ecological Inference Problem_.
     Princeton: Princeton University Press.

     Jonathan Wakefield. 2001. ``Ecological Inference for 2 x 2
     Tables,'' Center for Statistics and the Social Sciences Working
     Paper  no. 12. University of Washington.

     Kevin M. Quinn. 2002. ``Ecological Inference in the Presence of
     Temporal Dependence.'' Paper prepared for Ecological Inference
     Conference, Harvard University, June 17-18, 2002.

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

     'MCMChierEI', 'MCMCdynamicEI','tomogplot'

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

     ## Not run: 
     ## simulated data example 1
     set.seed(3920)
     nases in the 2000 term.

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

     data(SupremeCourt)

_F_o_r_m_a_t:

     The dataframe has contains data for justices Rehnquist, Stevens,
     O'Connor, Scalia, Kennedy, Souter, Thomas, Ginsburg, and Breyer
     for the 2000 term of the U.S. Supreme Court.  It contains data
     from 43 non-unanimous cases. The votes are coded liberal (1) and
     conservative (0) using the protocol of Spaeth (2003).   The unit
     of analysis is the case citation (ANALU=0).  We are concerned with
     formally decided cases issued with written opinions, after full
     oral argument and cases decided by an equally divided vote
     (DECTYPE=1,5,6,7).

_S_o_u_r_c_e:

     Harold J. Spaeth. 2005. _Original United States Sup