analyze2x2xK(SimpleTable)
analyze2x2xK()所属R语言包：SimpleTable
Analyze 2 x 2 x K Table in the Presence of Unmeasured Confounding
在测量的干扰因素的存在，分析2×2×K表
译者：生物统计家园网 机器人LoveR
描述----------Description----------
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
analyze2x2xK执行的因果贝叶斯分析的一个2×2 X K表，在该表中，假设测量的干扰因素是本。二进制处理变量表示X = 0（对照组），1（治疗）;二进制结果变量表示Y = 0（失败），1（成功），以及在分类测量的混杂因素表示W=0, ..., K-1。的符号和术语
用法----------Usage----------
analyze2x2xK(SimpleTableList, Wpriorvector)
参数----------Arguments----------
参数：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.
列表KSimpleTable对象通过使用analyze2x2分析K有条件(X,Y)表，给每个级别测量的混杂因素W形成。
参数：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 Wpriorvector corresponds to the kth element of W.
K矢量提供的参数前的Dirichlet φ其中phi_k = Pr(W=k)k=0, ..., K-1。 kWpriorvector个元素对应的k个元素W。
Details
详细信息----------Details----------
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.
analyze2x2xK执行一个2×2×K表奎恩（2008年）中描述的贝叶斯分析。 summary和plot方法可以用来检查输出。
值----------Value----------
An object of class SimpleTable.
对象的类SimpleTable。
（作者）----------Author(s)----------
Kevin M. Quinn
参考文献----------References----------
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
参见----------See Also----------
ConfoundingPlot, analyze2x2, ElicitPsi, summary.SimpleTable, plot.SimpleTable
ConfoundingPlot，analyze2x2，ElicitPsi，summary.SimpleTable，plot.SimpleTable
实例----------Examples----------
## Not run: [＃不运行：]
## Example from Quinn (2008)[＃示例从奎因（2008）]
## (original data from Oliver and Wolfinger. 1999. [＃（原奥利弗和Wolfinger 1999年的数据。]
## ``Jury Aversion and Voter Registration.'' [“陪审团的厌恶和选民登记。]
## American Political Science Review. 93: 147-152.)[＃美国政治科学评论。 93：147-152）。]
##[＃]
##[＃]
## W=0[＃W = 0]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 1 21[＃X = 0，1月21日]
## X=1 10 93[＃X = 1 10 93]
##[＃]
##[＃]
## W=1[＃W = 1]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 5 32[＃X = 0 5 32]
## X=1 27 92[＃X = 1 27 92]
##[＃]
##[＃]
## W=2[＃W = 2]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 4 44[＃X = 0 4 44]
## X=1 52 186[＃X = 1 52 186]
##[＃]
##[＃]
## W=3[＃W = 3]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 7 20[＃X = 0 7月20日]
## X=1 19 47[＃X = 1 19 47]
##[＃]
##[＃]
## W=4[＃W = 4]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 2 26[＃X = 0 2月26日]
## X=1 6 55[＃X = 1 6 55]
##[＃]
## a prior belief in an essentially negative monotonic treatment effect [＃先前的信念，基本上是负面的单调的治疗效果]
## with the largest effects among those for whom W <= 2[＃与影响最大的是那些对他们来说，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)[＃（不执行）]
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注：
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发表于 2012-9-30 02:53:40
