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 * 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

Details

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.

References

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.

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 0.5-2 Index]