sensitivity(twang)
sensitivity()所属R语言包：twang
Sensitivity analysis
敏感度分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Produces a table to help the user assess the extent to which a hidden bias might remove any differences observed in the propensity score
生成一个表，以帮助用户评估在何种程度上隐藏的偏见可能会删除任何差异的倾向得分
用法----------Usage----------
sensitivity(ps1,
data,
outcome,
order.by.importance = TRUE,
verbose = TRUE)
参数----------Arguments----------
参数：ps1
a ps object as returned from ps
ps对象作为返回ps
参数：data
the data frame used to fit ps1
数据框，以适应ps1
参数：outcome
a character string indicating the name of the variable in data to use as the outcome
一个字符串，表示data使用的结果的变量名
参数：order.by.importance
if TRUE then the variables are sorted by their relative influence in the gbm.object used to create ps1
如果TRUE然后进行排序的变量的相对影响中的gbm.object创建ps1的
参数：verbose
if TRUE, lots of information will be printed to monitor the the progress of the fitting
如果TRUE，信息将被打印到监察的进展配件，
Details
详细信息----------Details----------
This function implements the sensitivity analysis described in Ridgeway (2006), Section 5.5. This analysis helps the user assess the extent to which a hidden bias might remove any differences observed in the propensity score analysis.
此功能实现了里奇韦（2006年），第5.5节中所描述的敏感性分析。这种分析可以帮助用户评估在何种程度上可能会删除一个隐藏的偏见的倾向得分差异分析。
If there is an important unobserved factor the odds than the correct propensity score weight is not w(x_i), as the propensity score model predicts, but actually w(x_i, z_i) where z represents the unobserved factor. Let a_i=w(x_i, z_i)/w(x_i). These a_i's give an estimate of g(a), the distribution of the multiplicative errors that we observe in the weights when excluding z_i. Changing the values of the a_i's will affect the treatment effect estimate if a is correlated with y, the outcome. The stronger the correlation the more sensitive the results will be to the hidden bias. sensitivity computes over control group subjects a modified estimate of E(Y_0|t=1).
如果有一个重要的观测不到的因素是不正确的倾向得分重量的几率比w(x_i)，倾向评分模型预测，但实际上w(x_i, z_i)其中z代表观测不到的因素。让我们a_i=w(x_i, z_i)/w(x_i)。这些a_i的给估计g(a)，分布，我们观察到在权重的乘法的错误时，不包括z_i。更改的值a_is将影响治疗效果的估计，如果a与y，结果。较强的相关性比较敏感的结果将是隐藏的偏见。 sensitivity计算，超过控制组受试者的E(Y_0|t=1)修改后的预算。
subject to the constraint that a_i ~ g(a) and cor(a_i, y_i) = ρ.
受约束的a_i ~ g(a)和cor(a_i, y_i) = ρ。
Several g(a)'s are considered by removing each variable from the propensity score model in turn and computing the ratio of the original weights to the weights with the variable removed. Several choices for ρ are also considered, making ρ as large as possible, as small as possible, and solving for the “break even” ρ, the
几个g(a)的被认为是通过除去每个变量反过来从倾向评分模型和计算的原始权重，以除去与变量的权重的比率。几种选择ρ也认为，ρ尽可能大，尽可能的小，解决“收支平衡”ρ，
值----------Value----------
Returns a list where each component contains the sensitivity analysis for each stop.method used in fitting ps1. Each component contains a data frame with a row for each variable in the original propensity score model. The columns are
返回一个列表，其中每个组件包含了敏感性分析，为每一个stop.method接头ps1。每个组件都包含有一排数据框中每个变量的原始倾向得分模式。列
参数：var
the name of the variable excluded from the model
排除从模型中的变量的名称
参数：E0
the estimated E(Y_0|t=1) with var excluded from the propensity score model
估计E(Y_0|t=1)与var排除的倾向评分模型
参数：a.min,a.max
the smallest and largest values of a observed
最小和最大的值a观察
参数：a.cor
the observed correlation between a and y
所观察到的相关性在a和y之间
参数：a.mincor,a.maxcor
the smallest and largest values of ρ possible
ρ可能的最大和最小的值
参数：minE0,maxE0
the smallest and largest values of estimated E(Y_0|t=1) possible
估计E(Y_0|t=1)可能的最大和最小的值
参数：breakeven.cor
the break even correlation (see Details section)
盈亏平衡相关（见详图）
（作者）----------Author(s)----------
Greg Ridgeway <a href="mailto:gregr@rand.org">gregr@rand.org</a>
参考文献----------References----------
post-traffic stop outcomes using propensity scores,” Journal of
参见----------See Also----------
See ps for an example
见ps的一个例子
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 13:07:58
