R: Dynamic Tomography Plot

dtomogplot {MCMCpack} | R Documentation

## Dynamic Tomography Plot

### Description

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

### Usage

    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", ...)

### Arguments

`r0` | An (ntables \times 1) vector of row sums from row 0.
`r1` | An (ntables \times 1) vector of row sums from row 1.
`c0` | An (ntables \times 1) vector of column sums from column 0.
`c1` | An (ntables \times 1) vector of column sums from column 1.
`time.vec` | Vector of time periods that correspond to the elements of r_0, r_1, c_0, and c_1.
`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

### Details

Consider the following partially observed 2 by 2 contingency table:

| | Y=0 | | Y=1 | |
--------- | --------- | --------- | ---------
X=0 | | Y_0 | | | | r_0
--------- | --------- | --------- | ---------
X=1 | | Y_1 | | | | r_1
--------- | --------- | --------- | ---------
| | c_0 | | c_1 | | N

where r_0, r_1, c_0, c_1, and N are non-negative integers that are observed. The interior cell entries are not observed. It is assumed that Y_0|r_0 \sim \mathcal{B}inomial(r_0, p_0) and Y_1|r_1 \sim \mathcal{B}inomial(r_1, p_1).

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

### References

Gary King, 1997. A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.

Jonathan C. Wakefield. 2004. “Ecological Inference for 2 x 2 Tables.” Journal of the Royal Statistical Society, Series A. 167(3): 385445.

Kevin Quinn. 2004. “Ecological Inference in the Presence of Temporal Dependence." In Ecological Inference: New Methodological Strategies. Gary King, Ori Rosen, and Martin A. Tanner (eds.). New York: Cambridge University Press.

### See Also

`MCMChierEI`, `MCMCdynamicEI`,`tomogplot`

### Examples

    ## 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)

[Package MCMCpack version 1.4-0 Index]