Description Usage Arguments Details Author(s) References See Also Examples
plot.SimpleTable
summarizes a SimpleTable
object by plotting the psterior density of the prima facie and
sensitivity analysis causal effects.
1 2 3 4 5 6 7 8 9  ## S3 method for class 'SimpleTable'
plot(x, estimand = c("ATE", "ATT", "ATC", "RR", "RRT", "RRC",
"logRR", "logRRT", "logRRC"),
percent = 95, plot.bounds = TRUE, plot.pf = TRUE,
plot.sens = TRUE, plot.prior = FALSE,
color.bounds = "cyan",
color1.pf = "lawngreen", color2.pf = "green",
color1.sens = "magenta3", color2.sens = "purple4",
color.prior = "lightgray", ymax = NULL, ...)

x 
An object of class 
estimand 
The causal estimand of interest. Options include:

percent 
A number between 0 and 100 (exclusive) giving the size of the highest posterior density regions to be calculated and plotted. Default value is 95. 
plot.bounds 
Logical value indicating whether the largesample nonparametric bounds should be plotted. Default value is 
plot.pf 
Logical value indicating whether the posterior
density of the prima facie causal effect should be plotted. Default
value is 
plot.sens 
Logical value indicating whether the posterior
density of the sensitivity analysis causal effect should be plotted. Default
value is 
plot.prior 
Logical value indicating whether the
prior density of the causal effect of interest should be plotted. Default
value is 
color.bounds 
The color of the line segment depicting the
largesample nonparametric bounds. Default value is 
color1.pf 
The color of the prima facie posterior density in
regions outside the 
color2.pf 
The color of the prima facie posterior density in
regions inside the 
color1.sens 
The color of the sensitivity analysis posterior
density in regions outside the 
color2.sens 
The color of the sensitivity analysis posterior
density in regions inside the 
color.prior 
The color of the prior density of the causal
effect of interest. Default value is 
ymax 
The maximum height of the yaxis. If 
... 
Other arguments to be passed. 
See Quinn (2008) for the a description of these plots along with the associated terminology and notation.
Kevin M. Quinn
Quinn, Kevin M. 2008. “What Can Be Learned from a Simple Table: Bayesian Inference and Sensitivity Analysis for Causal Effects from 2 x 2 and 2 x 2 x K Tables in the Presence of Unmeasured Confounding.” Working Paper.
ConfoundingPlot
, analyze2x2
, analyze2x2xK
, ElicitPsi
, summary.SimpleTable
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  ## Not run:
## Example from Quinn (2008)
## (original data from Oliver and Wolfinger. 1999.
## ``Jury Aversion and Voter Registration.''
## American Political Science Review. 93: 147152.)
##
## Y=0 Y=1
## X=0 19 143
## X=1 114 473
##
## a prior belief in an essentially negative monotonic treatment effect
S.mono < analyze2x2(C00=19, C01=143, C10=114, C11=473,
a00=.25, a01=.25, a10=.25, a11=.25,
b00=0.02, c00=10, b01=25, c01=3,
b10=3, c10=25, b11=10, c11=0.02)
## ATE (the default)
plot(S.mono)
## ATC instead of ATE
plot(S.mono, estimand="ATC")
## different colors
plot(S.mono, estimand="ATC", color1.pf="red", color2.pf="blue",
color1.sens="gray", color2.sens="orange")
## End(Not run)

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