ps(twang)
ps()所属R语言包：twang
Propensity score estimation
估计倾向得分
译者：生物统计家园网 机器人LoveR
描述----------Description----------
ps calculates propensity scores and diagnoses them using a variety of methods, but centered on using boosted logistic regression as
ps计算倾向得分和诊断，他们使用的是不同的方法，但集中在提高逻辑回归
用法----------Usage----------
ps(formula = formula(data),
data,
n.trees = 10000,
interaction.depth = 3,
shrinkage = 0.01,
bag.fraction = 1.0,
perm.test.iters=0,
print.level = 2,
iterlim = 1000,
verbose = TRUE,
estimand = "ATE",
stop.method = c("ks.mean", "es.mean"),
sampw = NULL, ...)
参数----------Arguments----------
参数：formula
A formula for the propensity score model with the treatment indicator on the left side of the formula and the potential confounding variables on the right side.
为下式的左侧和潜在混杂变量的右侧与治疗指标的倾向评分模型的公式。
参数：data
The dataset, includes treatment assignment as well as covariates
该数据集，包括治疗分配以及协变量
参数：n.trees
number of gbm iterations passed on to gbm
GBM的迭代通过gbm
参数：interaction.depth
interaction.depth passed on to gbm
interaction.depth到gbm
参数：shrinkage
shrinkage passed on to gbm
shrinkage到gbm
参数：bag.fraction
bag.fraction passed on to gbm
bag.fraction到gbm
参数：perm.test.iters
a non-negative integer giving the number of iterations of the permutation test for the KS statistic. If perm.test.iters=0 then the function returns an analytic approximation to the p-value. Setting perm.test.iters=200 will yield precision to within 3% if the true p-value is 0.05. Use perm.test.iters=500 to be within 2%
一个非负的整数，给出的KS统计量的置换试验的数目的迭代。如果perm.test.iters=0那么该函数返回的解析近似的p值。设置perm.test.iters=200将产生精度在3％以内，如果真正的p值是0.05。使用perm.test.iters=500在2％以内
参数：print.level
the amount of detail to print to the screen
量的详细信息打印到屏幕上
参数：iterlim
maximum number of iterations for the direct optimization
最大的迭代次数的直接优化
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，
参数：estimand
The causal effect of interest. Options are "ATE" (average treatment effect), which attempts to estimate the change in the outcome if the treatment were applied to the entire population versus if the control were applied to the entire population, or "ATT" (average treatment effect on the treated) which attempts to estimate the analogous effect, averaging only over the treated population.
因果关系的兴趣。选项"ATE"（平均治疗效果），它试图估计的结果，如果治疗被应用到整个人口与控制，适用于整个人口的变化，或"ATT"（平均尝试估计类似的效果，只有在处理人口平均处理）的治疗效果。
参数：stop.method
A method or methods of measuring and summarizing balance across pretreatment variables. Current options are ks.mean, ks.max, es.mean, and es.max. ks refers to the Kolmogorov-Smirnov statistic and es refers to standardized effect size. These are summarized across the pretreatment variables by either the maximum (.max) or the mean (.mean).
的方法或方法的测量和总结预处理变量之间的平衡。目前的期权是ks.mean，ks.max，es.mean和es.max。 ks是指柯尔莫哥洛夫 - 斯米尔诺夫统计和es是指标准化规模效应。总结了这些跨预处理变量由最大（.max）或平均值（.mean）。
参数：sampw
Optional sampling weights.
可选的取样权重。
参数：...
Additional arguments. Not currently used.
其他参数。当前未使用。
Details
详细信息----------Details----------
formula should be something like "treatment ~ X1 + X2 + X3". The treatment variable should be a 0/1 indicator. There is no need to specify interaction terms in the formula. interaction.depth controls the level of interactions to allow in the propensity score model.
formula应该是类似“治疗~X1 + X2 + X3”。处理变量应该是一个0/1的指标。没有需要指定交互作用项公式中的。 interaction.depth控制水平的互动，允许在倾向评分模型。
Note that — unlike earlier versions of twang — plotting functions are no longer included in the ps() function. See
请注意 - 不同于早期版本的twang - 绘图功能不再包含在ps()功能。看
值----------Value----------
Returns an object of class ps, a list containing
返回一个对象类ps，一个列表，其中包含
参数：gbm.obj
The returned gbm object
返回的gbm对象
参数：treat
The treatment variable.
处理变量。
参数：desc
a list containing balance tables for each method selected in stop.methods. Includes a component for the unweighted analysis names “unw”. Each desc component includes a list with the following components
一个列表，其中包含资产负债表的每个方法中选择stop.methods。包括未加权的分析“UNW”的一个组成部分。每个desc组件包括以下组件列表
essThe effective sample size of the control group
essThe有效样本量的对照组
n.treatThe number of subjects in the treatment group
治疗组的科目n.treatThe
n.ctrlThe number of subjects in the control group
在对照组中的一些科目n.ctrlThe
max.esThe largest effect size across the covariates
max.esThe对面的协变量的影响最大尺寸
mean.esThe mean absolute effect size
mean.esThe意味着绝对的规模效应
max.ksThe largest KS statistic across the covariates
max.ksThe协变量之间最大的KS统计
mean.ksThe average KS statistic across the covariates
mean.ksThe平均KS协变量之间统计
bal.taba (potentially large) table summarizing the quality of the weights for equalizing the distribution of features across the two groups. This table is best extracted using the bal.table method. See the help for bal.table for details on the table's contents
bal.taba（潜在的大）表总结用于均衡跨越两个组的分布功能的权重的质量。此表是最好的提取，使用bal.table方法。请参阅帮助bal.table的详细信息，表的内容
n.treesThe estimated optimal number of gbm iterations to optimize the loss function for the associated stop.methods
n.treesThe最佳估计数gbm迭代优化的损失函数相关的stop.methods
psa data frame containing the estimated propensity scores. Each column is associated with one of the methods selected in stop.methods
PSA数据框包含的估计倾向得分。每一列都被与在stop.methods选择的方法之一相关联
wa data frame containing the propensity score weights. Each column is associated with one of the methods selected in stop.methods. If sampling weights are given then these are incorporated into these weights.
WA数据框包含的倾向得分权重。每一列都被与在stop.methods选择的方法之一相关联。如果取样权重，那么这些被纳入这些权重。
estimandThe estimand of interest (ATT or ATE).
（ATT或ATE）estimandThe estimand的利益。
参数：datestamp
Records the date of the analysis
记录的分析的日期
参数：parameters
Saves the ps call
保存ps调用
参数：alerts
Text containing any warnings accumulated during the estimation
文本包含任何警告期间积累的估计
参数：iters
A sequence of iterations used in the GBM fits used by plot function.
在GBM的迭代序列适合用于的plot功能。
参数：balance
The balance measures for the pretreatment covariates, with a column for each stop.method.
平衡措施的预处理协变量，一列每个stop.method。
参数：n.trees
Maximum number of trees considered in GBM fit.
的最大数量考虑GBM适合的树木。
参数：data
Data as specified in the data argument.
data参数中指定的数据。
（作者）----------Author(s)----------
Greg Ridgeway <a href="mailto:gregr@rand.org">gregr@rand.org</a>,
Dan McCaffrey <a href="mailto:danielm@rand.org">danielm@rand.org</a>,
Andrew Morral <a href="mailto:morral@rand.org">morral@rand.org</a>,
Lane Burgette <a href="mailto:burgette@rand.org">burgette@rand.org</a>
参考文献----------References----------
with Boosted Regression for Evaluating Adolescent Substance Abuse Treatment,” Psychological Methods 9(4):403-425.
参见----------See Also----------
gbm
gbm
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 13:07:44
