问题描述
是否有创建置信区间的现有函数来自 svyby 对象的比例(在我的例子中是 survey 包中二进制项的交叉表).我经常比较各组之间的比例，如果有一个可以提取置信区间的函数(使用调查函数 svyciprop 而不是 confint)会非常方便.下面的示例显示了我想要实现的目标.
Is there an existing function that creates confidence intervalsfrom a svyby object for proportions (in my case a crosstab for a binary item in the survey package). I often compare proportions across groups, and it would be very handy to have a function that can extract confidence intervals (with the survey function svyciprop rather than confint). The example below shows what I'd like to achieve.
加载数据
library(survey)
library(weights)
data(api)
apiclus1$both<-dummify(apiclus1$both)[,1]#Create dummy variable
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
创建一个 svyby 对象，比较 stype 中变量两者"的比例
Create a svyby object which compares proportion of variable "both" across stype
b<-svyby(~both, ~stype, dclus1, svymean)
confint(b)#This works, but svyciprop is best in other cases, especially when proportion is close to 0 or 1
svyciprop(b)#This requires that you specify each level and a design object
是否可以创建一个函数(例如 byCI(b,method="likelihood")，它实现与 confint(b) 相同的功能，但使用 svyciprop?它基本上必须遍历 svyby 对象的每个级别并创建一个置信区间.到目前为止，我的尝试都没有成功.
Would it be possible to create a function (e.g. byCI(b,method="likelihood") which achieves the same as confint(b) but using svyciprop? It would basically have to go through each level of the svyby object and create a confidence interval. My attempts have been unsuccessful up to now.
可能有另一种解决方法，但我喜欢使用 svyby()，因为它快速且直观.
There may be another way around this, but I like using svyby() as it's quick and intuitive.
推荐答案
svyby() 有一个 vartype= 参数来指定您希望如何指定采样不确定性.使用 vartype="ci" 获得置信区间，例如
svyby() has a vartype= argument to specify how you want the sampling uncertainty specified. Use vartype="ci" to get confidence intervals, eg
svyby(~I(ell>0),~stype,design=dclus1, svyciprop,vartype="ci",method="beta")
很容易检查这是否与手动完成每个级别相同，例如，
It's easy to check that this gives the same as doing each level by hand, eg,
confint(svyciprop(~I(ell>0), design=subset(dclus1,stype=="E"),method="beta"))
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