Question

我试图从加权调查数据集中分析多重回答问题。我喜欢srvyr包，因为它允许我使用dplyr管道，但我无法找到有关如何处理多个响应问题的参考资料。

我有一个简单的数据集，可以查看不同的收入来源。以下是数据如何显示的示例

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，数据完全由数据组成。如果首先必须创建调查对象，则使用srvyr

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。有关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。但我在R

中找不到类似的

Answer 1

查看https://cran.r-project.org/web/packages/srvyr/vignettes/srvyr-vs-survey.html

的proportions by group块

Answer 2

您可以使用group_by()和dplyr R软件包提供的便捷srvyr和变量选择语法。

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

使用srvyr进行加权调查数据的多重响应分析

2 个答案: