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)
     n <- 100
     r0 <- rpois(n, 2000)
     r1 <- round(runif(n, 100, 4000))
     p0.true <- pnorm(-1.5 + 1:n/(n/2))
     p1.true <- pnorm(1.0 - 1:n/(n/4))
     y0 <- rbinom(n, r0, p0.true)
     y1 <- rbinom(n, r1, p1.true)
     c0 <- y0 + y1
     c1 <- (r0+r1) - c0

     ## plot data
     dtomogplot(r0, r1, c0, c1, delay=0.1)

     ## simulated data example 2
     set.seed(8722)
     n <- 100
     r0 <- rpois(n, 2000)
     r1 <- round(runif(n, 100, 4000))
     p0.true <- pnorm(-1.0 + sin(1:n/(n/4)))
     p1.true <- pnorm(0.0 - 2*cos(1:n/(n/9)))
     y0 <- rbinom(n, r0, p0.true)
     y1 <- rbinom(n, r1, p1.true)
     c0 <- y0 + y1
     c1 <- (r0+r1) - c0

     ## plot data
     dtomogplot(r0, r1, c0, c1, delay=0.1)
     ## End(Not run)

