使用 srvyr 对加权调查数据进行多重响应分析 [英] Multiple response analysis in weighted survey data using srvyr

问题描述

我正在尝试从加权调查数据集中分析多选题.我喜欢 srvyr 包，因为它允许我使用 dplyr 管道，但我找不到有关如何处理多重响应问题的参考资料.

I'm trying to analyse a multiple response question from a weighted survey dataset. I like the srvyr package because it allows me to use the dplyr pipes, but I can't find the reference material on how to handle multiple response questions.

我有一个简单的数据集，查看不同的收入来源.以下是数据的示例

I have a simple dataset looking at different sources of income. Here's an example of how the data looks like

ID <- c(1,2,3,4,5,6,7,8,9,10)
rent_income <- c("Yes", "Yes", "No", "Yes", "No", "Yes", "No", "Yes", "No", "No")
salary_income <- c( "No", "Yes", "No", "Yes", "No", "Yes", "Yes", "No", "Yes", "No")
other_income <- c( "No", "Yes", "No", "No", "No", "No", "Yes", "No", "No", "No")
survey_weights <- c(0.6, 1.2 , 1.1 , 0.7 , 2.4 , 1.1 , 0.3 , 0.6 , 1.0 , 0.8)
df<-data.frame(ID, rent_income, salary_income, other_income, survey_weights)

请注意，数据是完全组成的.使用 srvyr 如果首先必须创建一个调查对象

Note that the data is entirely made up. With srvyr if first have to create a survey object

weighted_dataset <- df %>% as_survey_design(ids=ID, weights=survey_weights)

现在我想计算具有不同收入类型的样本的加权百分比.关于如何做到这一点的任何想法?在 Stata 中有一个名为 mr_tab 的函数.但是我在 R 中找不到类似的

Now I would want to calculate the weighted percentage of the sample that has different types of incomes. Any ideas on how to do that? In Stata there is a function called mr_tab . But I can't find a similar one in R

推荐答案

您可以通过 dplyr 和 使用方便的 group_by() 和变量选择语法>srvyr R 包.

You could use the convenient group_by() and variable selection syntax available through the dplyr and srvyr R packages.

weighted_dataset %>%
  # Organize the data into groups defined by each combination of the income variables
    group_by_at(vars(ends_with("_income"))) %>%
  # For categorical variables, this calculates estimates of percentages
    summarize(Percent = survey_mean())

> # A tibble: 6 x 5
>  rent_income salary_income other_income Percent Percent_se
>  <fct>       <fct>         <fct>          <dbl>      <dbl>
> 1 No          No            No             1          0    
> 2 No          Yes           No             0.769      0.265
> 3 No          Yes           Yes            0.231      0.265
> 4 Yes         No            No             1          0    
> 5 Yes         Yes           No             0.6        0.312
> 6 Yes         Yes           Yes            0.40       0.312

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