Description Usage Arguments Details Value Author(s) References See Also Examples
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).
1 | analyze2x2xK(SimpleTableList, Wpriorvector)
|
SimpleTableList |
A list of K |
Wpriorvector |
K-vector giving the parameters of the
Dirichlet prior for φ where phi_k =
Pr(W=k) for k=0, ..., K-1. The kth
element of |
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
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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | ## 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)
|
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