Description Usage Arguments Details Value Author(s) References Examples
‘plot’ method for the class ‘ei’.
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x 
An 
... 
A list of options to return in graphs. See values below. 
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.
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 β_i^b and β_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 β_i^b's with whiskers. 
betaw 
Density estimate (i.e., a smooth version of a histogram) of point estimates of β_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_iX_i) and conditional 80\% confidence intervals. See Figure 10.3, page 206. 
xtfitg 

estsims 
All the simulated β_i^b's by all the simulated β_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 β_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 β_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 precinctlevel. Requires 
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 β_i^b and β_i^w and a plot of the simulated values of β_i^b and β_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). 
Gary King <<email: king@harvard.edu>> and Molly Roberts <<email: molly.e.roberts@gmail.com>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
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Loading required package: eiPack
Loading required package: MASS
Loading required package: coda
Loading required package: msm
[1] "Running 2x2 ei"
Maximizing likelihood
Importance Sampling..
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