ConfoundingPlot(SimpleTable)
ConfoundingPlot()所属R语言包：SimpleTable
Confounding Plot of Quinn (2008)
混杂图奎因（2008）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
ConfoundingPlot implements the "confounding plot" discussed in Quinn (2008). This plot displays, in the context of binary treatment (X = 0: control, 1: treatment) and binary outcome (Y = 0: failure, 1: success), all types of unmeasured confounding that would keep a true causal effect of interest within some user-defined
ConfoundingPlot实现奎因（2008）在讨论“混淆图”。此图显示，在上下文的二进制处理（X = 0：控制，1：治疗）和二进制结果（Y = 0：失败，1：成功），所有类型测量的干扰因素，一些用户定义之内保持一个真正的因果关系
用法----------Usage----------
ConfoundingPlot(theta00, theta01, theta10, theta11,
conditioning = c("None", "Treated", "Control"),
PrY1.setX0 = NULL, PrY1.setX1 = NULL,
PrY1.setX0.withinTreated = NULL,
PrY1.setX1.withinControl = NULL,
epsilon = 0.025, color = "black", legend = FALSE)
参数----------Arguments----------
参数：theta00
The observed joint probability that X is control and Y is failure (Pr(X=0, Y=0)). In a 2 x 2 table in which C00 is the number of observations in the (X=0, Y=0) cell and in which there are C total observations one can consistently estimate theta00 with C00/C.
所观察到的联合概率X是控制和Y是故障（Pr(X=0, Y=0)）。在一个2×2表中C00（X=0，Y=0）单元，其中有C总观察人能够一贯估计的若干意见theta00与C00/C。
参数：theta01
The observed joint probability that X is control and Y is success (Pr(X=0, Y=1)). In a 2 x 2 table in which C01 is the number of observations in the (X=0, Y=1) cell and in which there are C total observations one can consistently estimate theta01 with C01/C.
所观察到的联合概率，X是控制和Y是成功（Pr(X=0, Y=1)），的。在一个2×2表中C01（X=0，Y=1）单元，其中有C总观察人能够一贯估计的若干意见theta01与C01/C。
参数：theta10
The observed joint probability that X is treatment and Y is failure (Pr(X=1, Y=0)). In a 2 x 2 table in which C10 is the number of observations in the (X=1, Y=0) cell and in which there are C total observations one can consistently estimate theta10 with C10/C.
所观察到的联合概率X是治疗和Y失败（Pr(X=1, Y=0)）。在一个2×2表中C10（X=1，Y=0）单元，其中有C总观察人能够一贯估计的若干意见theta10与C10/C。
参数：theta11
The observed joint probability that X is treatment and Y is success (Pr(X=1, Y=1)). In a 2 x 2 table in which C11 is the number of observations in the (X=1, Y=1) cell and in which there are C total observations one can consistently estimate theta11 with C1/C.
所观察到的联合概率，X是治疗和Y是成功（Pr(X=1, Y=1)）。在一个2×2表中C11（X=1，Y=1）单元，其中有C总观察人能够一贯估计的若干意见theta11与C1/C。
参数：conditioning
A string detailing whether the post-intervention distribution, and hence the estimand of interest, is restricted to a particular subgroup. Possible values are: None, Treated, and Control. If conditioning = None then the post-intervention distribution is for all units. This is consistent with the causal estimand being the average treatment effect (ATE). If conditioning = Treated then the post-intervention is calculated only for just the treated units. This is consistent with the causal estimand being the average treatment effect within the treated (ATT). Finally, if conditioning = Control then the post-intervention distribution is calculated for just the control units. this is consistent with the causal estimand being the average treatment effect within the controls (ATC). Default is None.
的一个字符串，详细说明介入后的分布，并因此景点的estimand，是否被限制在一个特定的子群。可能的值有：None，Treated和Control。如果conditioning = None干预后分配的所有单位。这是与的因果estimand是（ATE）的平均处理效果一致。如果conditioning = Treated干预后，只计算处理后的单位。这是与的因果estimand是内的处理（ATT）的平均处理效果一致。最后，如果conditioning = Control干预后分布的计算方法只是控制单元。这是符合内的控制（ATC）的的因果estimand的平均处理效果。默认是None。
参数：PrY1.setX0
Optional value giving the assumed probability that a randomly chosen unit will have Y=1 (success) if its X value is set to 0 (control) by outside intervention. If PrY1.setX0 = NULL (the default) then PrY1.setX0 is set to the observed conditional probability that Y is 1 given that X is 0. In the terms of Quinn (2008), the reference distribution is the prima facie post-intervention distribution. Setting PrY1.setX0 to some non-NULL value allows one to use reference distributions other than the prima facie post-intervention distribution. This is useful if one wants to start with a particular value for ATE (that is not the prima facie ATE) and see how unmeasured confounding might affect that inference. Only applicable if conditioning = None.
可选值的假设的概率，一个随机选择的单元将具有Y=1（成功），如果它的X值被设置为0（控制）由外部干预。如果PrY1.setX0 = NULL（默认值），然后PrY1.setX0是观察到的条件概率，Y是1X是0。奎恩（2008年）的条款，参考分布的初步干预后分配。设置PrY1.setX0一些非NULL值允许使用的参考分布以外的初步干预后分配。这是非常有用的，如果想启动一个特定的值适用于ATE（即没有表面证据ATE）和如何测量的干扰因素可能会影响该推论。仅适用conditioning = None。
参数：PrY1.setX1
Optional value giving the assumed probability that a randomly chosen unit will have Y=1 (success) if its X value is set to 1 (treatment) by outside intervention. If PrY1.setX1 = NULL (the default) then PrY1.setX1 is set to the observed conditional probability that Y is 1 given that X is 1. In the terms of Quinn (2008), the reference distribution is the prima facie post-intervention distribution. Setting PrY1.setX1 to some non-NULL value allows one to use reference distributions other than the prima facie post-intervention distribution. This is useful if one wants to start with a particular value for ATE (that is not the prima facie ATE) and see how unmeasured confounding might affect that inference. Only applicable if conditioning = None.
可选值的假设的概率，一个随机选择的单元将具有Y=1（成功），如果它的X值被设置为1（治疗）由外部干预。如果PrY1.setX1 = NULL（默认值），然后PrY1.setX1是观察到的条件概率，Y是1X是1。奎恩（2008年）的条款，参考分布的初步干预后分配。设置PrY1.setX1一些非NULL值允许使用的参考分布以外的初步干预后分配。这是非常有用的，如果想启动一个特定的值适用于ATE（即没有表面证据ATE）和如何测量的干扰因素可能会影响该推论。仅适用conditioning = None。
参数：PrY1.setX0.withinTreated
Optional value giving the assumed probability that a randomly chosen unit which received treatment would have Y=1 (success) if its X value were set to 0 (control) by outside intervention. If PrY1.setX0.withinTreated = NULL (the default) then PrY1.setX0.withinTreated is set to the observed conditional probability that Y is 1 given that X is 0. In the terms of Quinn (2008), the reference distribution is the prima facie post-intervention distribution. Setting PrY1.setX0.withinTreated to some non-NULL value allows one to use reference distributions other than the prima facie post-intervention distribution. This is useful if one wants to start with a particular value for ATT (that is not the prima facie ATT) and see how unmeasured confounding might affect that inference. Only applicable if conditioning = Treated.
可选值的假设的概率，随机选择一个单位接受治疗，将有Y=1（成功），如果其的X值设置为0（控制）的外部干预。如果PrY1.setX0.withinTreated = NULL（默认值），然后PrY1.setX0.withinTreated是观察到的条件概率，Y是1X是0。奎恩（2008年）的条款，参考分布的初步干预后分配。设置PrY1.setX0.withinTreated一些非NULL值允许使用的参考分布以外的初步干预后分配。这是非常有用的，如果想启动ATT（不是表面上的ATT）与一个特定的值，看看如何测量的干扰因素可能会影响该推论。仅适用conditioning = Treated。
参数：PrY1.setX1.withinControl
Optional value giving the assumed probability that a randomly chosen unit which received control would have Y=1 (success) if its X value were set to 1 (treatment) by outside intervention. If PrY1.setX1.withinControl = NULL (the default) then PrY1.setX1.withinControl is set to the observed conditional probability that Y is 1 given that X is 1. In the terms of Quinn (2008), the reference distribution is the prima facie post-intervention distribution. Setting PrY1.setX1.withinControl to some non-NULL value allows one to use reference distributions other than the prima facie post-intervention distribution. This is useful if one wants to start with a particular value for ATC (that is not the prima facie ATC) and see how unmeasured confounding might affect that inference. Only applicable if conditioning = Control.
可选值的假设的概率，随机选择一个单位收到的控制，将有Y=1（成功），如果它的X的值被设置为1（治疗）由外部干预。如果PrY1.setX1.withinControl = NULL（默认值），然后PrY1.setX1.withinControl是观察到的条件概率，Y是1X是1。奎恩（2008年）的条款，参考分布的初步干预后分配。设置PrY1.setX1.withinControl一些非NULL值允许使用的参考分布以外的初步干预后分配。这是非常有用的，如果一个人想开始与一个特定的值ATC（即表面证据ATC），看看如何测量的干扰因素可能会影响该推论。仅适用conditioning = Control。
参数：epsilon
A scalar or array of tolerance values between 0 and 1. The plot depicts all regions of the space of confounders for which the absolute difference between the true post-intervention distribution and the assumed post-intervention distribution is less than epsilon. See Quinn (2008) for details. If epsilon is an array then color (see below) must also be an array and the various tolerance regions will be overlaid in color.
一个标量或数组的公差值在0和1之间。的图描绘所有区域的空间的干扰因素，它的真正干预后的分布和假设的干预后分布之间的绝对差值小于epsilon。奎因（2008）。如果epsilon是一个数组，那么color（见下文）也必须是一个数组，该的各种公差区域将被覆盖的颜色。
参数：color
An array of colors for the plotting regions. color must have length equal to the length of epsilon (see above).
阵列的绘图区域的颜色。 color必须有epsilon（见上文）的长度的长度相等。
参数：legend
Logical value indicating whether a legend should be printed.
逻辑值，该值指示是否应印有一个传说。
Details
详细信息----------Details----------
For full details see Quinn (2008).
如需完整的详细信息，请参阅奎恩（2008年）。
（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----------
analyze2x2, analyze2x2xK, ElicitPsi,
analyze2x2，analyze2x2xK，ElicitPsi，
实例----------Examples----------
## 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）。]
##[＃]
## Y=0 Y=1[＃Y = 0，Y = 1]
## X=0 19 143[＃X = 0 19 143]
## X=1 114 473[＃X = 1 114 473]
##[＃]
C <- 19 + 143 + 114 + 473
theta00 <- 19/C
theta01 <- 143/C
theta10 <- 114/C
theta11 <- 473/C
## may have to adjust size of graphics device to make labels readable[＃可能需要调整大小的图形设备，使标签可读]
ConfoundingPlot(theta00=theta00, theta01=theta01,
theta10=theta10, theta11=theta11, legend=TRUE)
## same data but with various epsilons and a legend[＃相同的数据，但不同的Epsilon和一个传奇]
## may have to adjust size of graphics device to make labels readable[＃可能需要调整大小的图形设备，使标签可读]
ConfoundingPlot(theta00=theta00, theta01=theta01,
theta10=theta10, theta11=theta11,
epsilon=c(.01, .025, .05, .1),
color=c("black", "darkblue", "blue", "cyan"),
legend=TRUE)
## same data but reference distribution is now just within the treated[＃相同的数据，但现在只是在治疗的参考分布]
## may have to adjust size of graphics device to make labels readable[＃可能需要调整大小的图形设备，使标签可读]
ConfoundingPlot(theta00=theta00, theta01=theta01,
theta10=theta10, theta11=theta11,
conditioning="Treated", legend=TRUE)
## set PrY1.setX0 and PrY1.setX1 in order to get a reference[＃PrY1.setX0和PrY1.setX1，为了得到一个参考]
## post-intervention distribution that is consistent with [＃干预后的分布是一致的]
## ATE = -0.2 (note there are many ways to do this)[＃（ATE）-0.2（注：有很多方法可以做到这一点）]
## may have to adjust size of graphics device to make labels readable[＃可能需要调整大小的图形设备，使标签可读]
ConfoundingPlot(theta00=theta00, theta01=theta01,
theta10=theta10, theta11=theta11,
PrY1.setX0=.9, PrY1.setX1=.7,
legend=TRUE)
## another way to get ATE = -0.2[＃另一种方式来获得ATE = -0.2]
## may have to adjust size of graphics device to make labels readable[＃可能需要调整大小的图形设备，使标签可读]
ConfoundingPlot(theta00=theta00, theta01=theta01,
theta10=theta10, theta11=theta11,
PrY1.setX0=.85, PrY1.setX1=.65,
legend=TRUE)
## a way to get ATE = -0.2 that is impossible given the observed data[＃一种方式来获得ATE = -0.2观测到的数据，这是不可能的]
## (note the complete lack of any shaded regions in the left panel of plot)[＃（注意完全没有任何阴影区域在左侧面板中的图）]
## may have to adjust size of graphics device to make labels readable[＃可能需要调整大小的图形设备，使标签可读]
ConfoundingPlot(theta00=theta00, theta01=theta01,
theta10=theta10, theta11=theta11,
PrY1.setX0=.5, PrY1.setX1=.3,
legend=TRUE)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-30 02:53:46
