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 large-sample 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
large-sample 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 y-axis. 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: 147-152.)
##
## 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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