analyze2x2xK {SimpleTable} | R Documentation |
analyze2x2xK
performs a causal Bayesian analysis
of a 2 x 2 x K table in which it is assumed that unmeasured
confounding is present. The binary treatment variable is denoted
X = 0 (control), 1 (treatment); the binary
outcome variable is denoted Y = 0 (failure), 1
(success); and the categorical measured confounder is denoted
W=0, ..., K-1. The notation and terminology are
from Quinn (2008).
analyze2x2xK(SimpleTableList, Wpriorvector)
SimpleTableList |
A list of K SimpleTable objects
formed by using analyze2x2 to analyze the K conditional
(X,Y) tables given each level of the measured confounder
W. |
Wpriorvector |
K-vector giving the parameters of the
Dirichlet prior for phi where phi_k =
Pr(W=k) for k=0, ..., K-1. The kth
element of Wpriorvector corresponds to the kth element of
W. |
analyze2x2xK
performs the Bayesian analysis of a 2 x 2 x K
table described in Quinn (2008). summary
and plot
methods can be used to examine the output.
An object of class SimpleTable
.
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
, ElicitPsi
, summary.SimpleTable
, plot.SimpleTable
## 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.) ## ## ## W=0 ## Y=0 Y=1 ## X=0 1 21 ## X=1 10 93 ## ## ## W=1 ## Y=0 Y=1 ## X=0 5 32 ## X=1 27 92 ## ## ## W=2 ## Y=0 Y=1 ## X=0 4 44 ## X=1 52 186 ## ## ## W=3 ## Y=0 Y=1 ## X=0 7 20 ## X=1 19 47 ## ## ## W=4 ## Y=0 Y=1 ## X=0 2 26 ## X=1 6 55 ## ## a prior belief in an essentially negative monotonic treatment effect ## with the largest effects among those for whom W <= 2 S.mono.0 <- analyze2x2(C00=1, C01=21, C10=10, C11=93, 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) S.mono.1 <- analyze2x2(C00=5, C01=32, C10=27, C11=92, 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) S.mono.2 <- analyze2x2(C00=4, C01=44, C10=52, C11=186, 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) S.mono.3 <- analyze2x2(C00=7, C01=20, C10=19, C11=47, a00=.25, a01=.25, a10=.25, a11=.25, b00=0.02, c00=10, b01=15, c01=1, b10=1, c10=15, b11=10, c11=0.02) S.mono.4 <- analyze2x2(C00=2, C01=26, C10=6, C11=55, a00=.25, a01=.25, a10=.25, a11=.25, b00=0.02, c00=10, b01=15, c01=1, b10=1, c10=15, b11=10, c11=0.02) S.mono.all <- analyze2x2xK(list(S.mono.0, S.mono.1, S.mono.2, S.mono.3, S.mono.4), c(0.2, 0.2, 0.2, 0.2, 0.2)) summary(S.mono.all) plot(S.mono.all) ## End(Not run)