analyze2x2(SimpleTable)
analyze2x2()所属R语言包：SimpleTable
Analyze 2 x 2 Table in the Presence of Unmeasured Confounding
在测量的干扰因素分析2×2表
译者：生物统计家园网 机器人LoveR
描述----------Description----------
analyze2x2 performs a causal Bayesian analysis of a 2 x 2 table in which it is assumed that unmeasured confounding is present. The binary treatment variable is denoted X = 0 (control), 1 (treatment); and the binary outcome variable is denoted Y = 0 (failure), 1 (success). The
analyze2x2执行一个2×2表格中的假设测量的干扰因素存在的因果贝叶斯分析。处理变量表示的二进制X = 0（对照组），1（治疗）;，结果变量的二进制表示Y = 0（失败），1（成功）。 “
用法----------Usage----------
analyze2x2(C00, C01, C10, C11, a00, a01, a10, a11,
b00, b01, b10, b11, c00, c01, c10, c11, nsamp = 50000)
参数----------Arguments----------
参数：C00
The number of observations in (X=0, Y=0) cell of the table. In other words, the number of observations that received control and failed.
(X=0, Y=0)表的单元格的若干意见。换言之，观察接收到的控制和失败的数目。
参数：C01
The number of observations in (X=0, Y=1) cell of the table. In other words, the number of observations that received control and succeeded.
(X=0, Y=1)表的单元格的若干意见。换句话说，观察，控制和成功。
参数：C10
The number of observations in (X=1, Y=0) cell of the table. In other words, the number of observations that received treatment and failed.
(X=1, Y=0)表的单元格的若干意见。换言之，观察，接受治疗和失败的数目。
参数：C11
The number of observations in (X=1, Y=1) cell of the table. In other words, the number of observations that received treatment and succeeded.
(X=1, Y=1)表的单元格的若干意见。换句话说，观测值的数量，接受治疗和成功了。
参数：a00
One of four parameters (with a01, a10, and a11 governing the Dirichlet prior for theta (the joint probabilities of X and Y). This prior has the effect of adding a00 - 1 observations to the (X=0, Y=0) cell of the table.
其中的四个参数（用a01，a10和a11执政前的Dirichlet theta（X和Y的联合概率 ），这之前有添加a00 - 1观测到(X=0, Y=0)表的单元格的效果。
参数：a01
One of four parameters (with a00, a10, and a11 governing the Dirichlet prior for theta (the joint probabilities of X and Y). This prior has the effect of adding a01 - 1 observations to the (X=0, Y=1) cell of the table.
其中的四个参数（用a00，a10和a11执政前的Dirichlet theta（X和Y的联合概率 ），这之前有添加a01 - 1观测到(X=0, Y=1)表的单元格的效果。
参数：a10
One of four parameters (with a00, a01, and a11 governing the Dirichlet prior for theta (the joint probabilities of X and Y). This prior has the effect of adding a10 - 1 observations to the (X=1, Y=0) cell of the table.
其中的四个参数（用a00，a01和a11执政前的Dirichlet theta（X和Y的联合概率 ），这之前有添加a10 - 1观测到(X=1, Y=0)表的单元格的效果。
参数：a11
One of four parameters (with a00, a01, and a10 governing the Dirichlet prior for theta (the joint probabilities of X and Y). This prior has the effect of adding a11 - 1 observations to the (X=1, Y=1) cell of the table.
其中的四个参数（用a00，a01和a10执政前的Dirichlet theta（X和Y的联合概率 ），这之前有添加a11 - 1观测到(X=1, Y=1)表的单元格的效果。
参数：b00
One of two parameters (with c00) governing the beta prior for the distribution of potential outcome types within the (X=0, Y=0) cell of the table. This prior adds the same information as would be gained from observing b00 - 1 Helped units in the (X=0, Y=0) cell of the table.
其中的两个参数（用c00）执政前的测试可能的结果类型的分布在(X=0, Y=0)表的单元格。前增加了观察b00 - (X=0, Y=0)表的单元格的帮助单位将获得相同的信息。
参数：b01
One of two parameters (with c01) governing the beta prior for the distribution of potential outcome types within the (X=0, Y=1) cell of the table. This prior adds the same information as would be gained from observing b01 - 1 Always Succeed units in the (X=0, Y=1) cell of the table.
其中的两个参数（用c01）执政前的测试可能的结果类型的分布在(X=0, Y=1)表的单元格。这之前增加将获得相同的信息，从观察b01 - 1(X=0, Y=1)表的单元格中总是成功的单位。
参数：b10
One of two parameters (with c10) governing the beta prior for the distribution of potential outcome types within the (X=1, Y=0) cell of the table. This prior adds the same information as would be gained from observing b10 - 1 Hurt units in the (X=1, Y=0) cell of the table.
其中的两个参数（用c10）执政前的测试可能的结果类型的分布在(X=1, Y=0)表的单元格。前增加了观察b10 - (X=1, Y=0)表的单元格的伤害单位将获得相同的信息。
参数：b11
One of two parameters (with c11) governing the beta prior for the distribution of potential outcome types within the (X=1, Y=1) cell of the table. This prior adds the same information as would be gained from observing b11 - 1 Always Succeed units in the (X=1, Y=1) cell of the table.
其中的两个参数（用c11）执政前的测试可能的结果类型的分布在(X=1, Y=1)表的单元格。这之前增加将获得相同的信息，从观察b11 - 1(X=1, Y=1)表的单元格中总是成功的单位。
参数：c00
One of two parameters (with b00) governing the beta prior for the distribution of potential outcome types within the (X=0, Y=0) cell of the table. This prior adds the same information as would be gained from observing b00 - 1 Never Succeed units in the (X=0, Y=0) cell of the table.
其中的两个参数（用b00）执政前的测试可能的结果类型的分布在(X=0, Y=0)表的单元格。这之前增加将获得相同的信息，从观察b00 - 1(X=0, Y=0)表的单元格绝对不能得逞的单位。
参数：c01
One of two parameters (with b01) governing the beta prior for the distribution of potential outcome types within the (X=0, Y=1) cell of the table. This prior adds the same information as would be gained from observing c01 - 1 Hurt units in the (X=0, Y=1) cell of the table.
其中的两个参数（用b01）执政前的测试可能的结果类型的分布在(X=0, Y=1)表的单元格。前增加了观察c01 - (X=0, Y=1)表的单元格的伤害单位将获得相同的信息。
参数：c10
One of two parameters (with b10) governing the beta prior for the distribution of potential outcome types within the (X=1, Y=0) cell of the table. This prior adds the same information as would be gained from observing c10 - 1 Never Succeed units in the (X=1, Y=0) cell of the table.
其中的两个参数（用b10）执政前的测试可能的结果类型的分布在(X=1, Y=0)表的单元格。这之前增加将获得相同的信息，从观察c10 - 1(X=1, Y=0)表的单元格绝对不能得逞的单位。
参数：c11
One of two parameters (with b11) governing the beta prior for the distribution of potential outcome types within the (X=1, Y=1) cell of the table. This prior adds the same information as would be gained from observing b11 - 1 Helped units in the (X=1, Y=1) cell of the table.
其中的两个参数（用b11）执政前的测试可能的结果类型的分布在(X=1, Y=1)表的单元格。前增加了观察b11 - (X=1, Y=1)表的单元格的帮助单位将获得相同的信息。
参数：nsamp
Size of the Monte Carlo sample used to summarize the posterior.
蒙特卡罗样本的大小，使用后总结。
Details
详细信息----------Details----------
analyze2x2 performs the Bayesian analysis of a 2 x 2 table described in Quinn (2008). summary and plot
analyze2x2执行一个2×2表奎恩（2008年）中描述的贝叶斯分析。 summary和plot
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, analyze2x2xK, ElicitPsi, summary.SimpleTable, plot.SimpleTable
ConfoundingPlot，analyze2x2xK，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. [＃（奥利弗和Wolfinger 1999年的原始数据。]
## ``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）。]
##[＃]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 19 143[＃X = 0 19 143]
## X=1 114 473[＃X = 1 114 473]
##[＃]
## uniform prior on the potential outcome distributions[＃前的可能结果分布均匀]
S.unif <- analyze2x2(C00=19, C01=143, C10=114, C11=473,
a00=.25, a01=.25, a10=.25, a11=.25,
b00=1, c00=1, b01=1, c01=1,
b10=1, c10=1, b11=1, c11=1)
summary(S.unif)
plot(S.unif)
## 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]
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)
summary(S.mono)
plot(S.mono)
## End(Not run)[＃（不执行）]
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 02:53:33
