plot.ei {ei}

Plotting Ecological Inference Estimates

Description

‘plot’ method for the class ‘ei’.

Usage

## S3 method for class 'ei'
plot(x, ...)

Arguments

x

An ei object from the function ei.

...

A list of options to return in graphs. See values below.

Details

Returns any of a set of possible graphical objects, mirroring those in the examples in King (1997). Graphical option lci is a logical value specifying the use of the Law of Conservation of Ink, where the implicit information in the data is represented through color gradients, i.e. the color of the line is a function of the length of the tomography line. This can be passed as an argument and is used for “tomogD” and “tomog” plots.

Value

tomogD

Tomography plot with the data only. See Figure 5.1, page 81.

tomog

Tomography plot with ML contours. See Figure 10.2, page 204.

tomogCI

Tomography plot with 80\% confidence intervals. Confidence intervals appear on the screen in red with the remainder of the tomography line in yellow. The confidence interval portion is also printed thicker than the rest of the line. See Figure 9.5, page 179.

tomogCI95

Tomography plot with 95\% confidence intervals. Confidence intervals appear on the screen in red with the remainder of the tomography line in yellow. The confidence interval portion is also printed thicker than the rest of the line. See Figure 9.5, page 179.

tomogE

Tomography plot with estimated mean posterior \beta_i^b and \beta_i^w points.

tomogP

Tomography plot with mean posterior contours.

betab

Density estimate (i.e., a smooth version of a histogram) of point estimates of \beta_i^b's with whiskers.

betaw

Density estimate (i.e., a smooth version of a histogram) of point estimates of \beta_i^w's with whiskers.

xt

Basic X_i by T_i scatterplot.

xtc

Basic X_i by T_i scatterplot with circles sized proportional to N_i.

xtfit

X_i by T_i plot with estimated E(T_i|X_i) and conditional 80\% confidence intervals. See Figure 10.3, page 206.

xtfitg

xtfit with Goodman's regression line superimposed.

estsims

All the simulated \beta_i^b's by all the simulated \beta_i^w's. The simulations should take roughly the same shape of the mean posterior contours, except for those sampled from outlier tomography lines.

boundXb

X_i by the bounds on \beta_i^b (each precinct appears as one vertical line), see the lines in the left graph in Figure 13.2, page 238.

boundXw

X_i by the bounds on \beta_i^w (each precinct appears as one vertical line), see the lines in the right graph in Figure 13.2, page 238.

truth

Compares truth to estimates at the district and precinct-level. Requires truth in the ei object. See Figures 10.4 (page 208) and 10.5 (page 210).

movieD

For each observation, one tomography plot appears with the line for the particular observation darkened. After the graph for each observation appears, the user can choose to view the next observation (hit return), jump to a specific observation number (type in the number and hit return), or stop (hit "s" and return).

movie

For each observation, one page of graphics appears with the posterior distribution of \beta_i^b and \beta_i^w and a plot of the simulated values of \beta_i^b and \beta_i^w from the tomography line. The user can choose to view the next observation (hit return), jump to a specific observation number (type in the number and hit return), or stop (hit “s" and return).

Author(s)

Gary King <<email: [e-mail]>> and Molly Roberts <<email: [e-mail]>>

References

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

Examples

data(sample)
formula = t ~ x
dbuf <- ei(formula=formula, total="n",data=sample)
plot(dbuf, "tomog")
plot(dbuf, "tomog", "betab", "betaw", "xtfit")