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r - R 中 MI 数据的描述性统计：取 3

问题描述

作为一个 R 初学者，我发现要弄清楚如何计算多重插补数据的描述性统计数据非常困难（比运行其他一些基本分析，如相关性和回归更难）。

这些类型的问题以道歉开头（描述性统计（Means，StdDevs）使用多重估算数据：R）但尚未得到回答（https://stats.stackexchange.com/questions/296193/pooling-basic-descriptives- from-several-multiply-imputed-datasets-using-mice）或很快投下反对票。

这是对miceadds 函数的描述（https://www.rdocumentation.org/packages/miceadds/versions/2.10-14/topics/stats0），我发现很难用 mids 格式存储的数据进行跟踪.

我已经使用summary(complete(imp)) 获得了一些输出，例如均值、中值、最小值、最大值，但我很想知道如何获得额外的汇总输出（例如，偏斜/峰度、标准差、方差）。

插图从之前的海报中借​​用：

  > imp <- mice(nhanes, seed = 23109)

    iter imp variable
    1   1  bmi  hyp  chl
    1   2  bmi  hyp  chl
    1   3  bmi  hyp  chl
    1   4  bmi  hyp  chl
    1   5  bmi  hyp  chl
    2   1  bmi  hyp  chl
    2   2  bmi  hyp  chl
    2   3  bmi  hyp  chl

  > summary(complete(imp))
   age         bmi        hyp         chl     
   1:12   Min.   :20.40   1:18   Min.   :113  
   2: 7   1st Qu.:24.90   2: 7   1st Qu.:186  
   3: 6   Median :27.40          Median :199  
          Mean   :27.37          Mean   :194  
          3rd Qu.:30.10          3rd Qu.:218  
          Max.   :35.30          Max.   :284

有人愿意花时间说明如何使用 mids 对象来获得基本描述吗？

标签： rsummaryimputationr-mice

解决方案

以下是您可以执行的一些步骤，以更好地了解每个步骤之后 R 对象会发生什么。我还建议查看本教程： https ://gerkovink.github.io/miceVignettes/

library(mice)

# nhanes object is just a simple dataframe: 
data(nhanes)
str(nhanes)
#'data.frame':  25 obs. of  4 variables:
#  $ age: num  1 2 1 3 1 3 1 1 2 2 ...
#$ bmi: num  NA 22.7 NA NA 20.4 NA 22.5 30.1 22 NA ...
#$ hyp: num  NA 1 1 NA 1 NA 1 1 1 NA ...
#$ chl: num  NA 187 187 NA 113 184 118 187 238 NA ...

# you can generate multivariate imputation using mice() function
imp <- mice(nhanes, seed=23109)

#The output variable is an object of class "mids" which you can explore using str() function
str(imp)
# List of 17
# $ call           : language mice(data = nhanes)
# $ data           :'data.frame':  25 obs. of  4 variables:
#   ..$ age: num [1:25] 1 2 1 3 1 3 1 1 2 2 ...
# ..$ bmi: num [1:25] NA 22.7 NA NA 20.4 NA 22.5 30.1 22 NA ...
# ..$ hyp: num [1:25] NA 1 1 NA 1 NA 1 1 1 NA ...
# ..$ chl: num [1:25] NA 187 187 NA 113 184 118 187 238 NA ...
# $ m              : num 5
# ...
 # $ imp            :List of 4
  #..$ age: NULL
  #..$ bmi:'data.frame':    9 obs. of  5 variables:
  #.. ..$ 1: num [1:9] 28.7 30.1 22.7 24.9 30.1 35.3 27.5 29.6 33.2
  #.. ..$ 2: num [1:9] 27.2 30.1 27.2 25.5 29.6 26.3 26.3 30.1 30.1
  #.. ..$ 3: num [1:9] 22.5 30.1 20.4 22.5 27.4 22 26.3 27.4 35.3
  #.. ..$ 4: num [1:9] 27.2 22 22.7 21.7 25.5 27.2 24.9 30.1 22
  #.. ..$ 5: num [1:9] 28.7 28.7 20.4 21.7 25.5 22.5 22.5 25.5 22.7
#...


#You can extract individual components of this object using $, for example
#To view the actual imputation for bmi column
imp$imp$bmi
#       1    2    3    4    5
# 1  28.7 27.2 22.5 27.2 28.7
# 3  30.1 30.1 30.1 22.0 28.7
# 4  22.7 27.2 20.4 22.7 20.4
# 6  24.9 25.5 22.5 21.7 21.7
# 10 30.1 29.6 27.4 25.5 25.5
# 11 35.3 26.3 22.0 27.2 22.5
# 12 27.5 26.3 26.3 24.9 22.5
# 16 29.6 30.1 27.4 30.1 25.5
# 21 33.2 30.1 35.3 22.0 22.7

# The above output is again just a regular dataframe:
str(imp$imp$bmi)
# 'data.frame':  9 obs. of  5 variables:
#   $ 1: num  28.7 30.1 22.7 24.9 30.1 35.3 27.5 29.6 33.2
# $ 2: num  27.2 30.1 27.2 25.5 29.6 26.3 26.3 30.1 30.1
# $ 3: num  22.5 30.1 20.4 22.5 27.4 22 26.3 27.4 35.3
# $ 4: num  27.2 22 22.7 21.7 25.5 27.2 24.9 30.1 22
# $ 5: num  28.7 28.7 20.4 21.7 25.5 22.5 22.5 25.5 22.7

# complete() function returns imputed dataset:
mat <- complete(imp)

# The output of this function is a regular data frame:
str(mat)
# 'data.frame':  25 obs. of  4 variables:
# $ age: num  1 2 1 3 1 3 1 1 2 2 ...
# $ bmi: num  28.7 22.7 30.1 22.7 20.4 24.9 22.5 30.1 22 30.1 ...
# $ hyp: num  1 1 1 2 1 2 1 1 1 1 ...
# $ chl: num  199 187 187 204 113 184 118 187 238 229 ...

# So you can run any descriptive statistics you need with this object
# Just like you would do with a regular dataframe:
> summary(mat)
# age            bmi             hyp            chl       
# Min.   :1.00   Min.   :20.40   Min.   :1.00   Min.   :113.0  
# 1st Qu.:1.00   1st Qu.:24.90   1st Qu.:1.00   1st Qu.:187.0  
# Median :2.00   Median :27.50   Median :1.00   Median :204.0  
# Mean   :1.76   Mean   :27.48   Mean   :1.24   Mean   :204.9  
# 3rd Qu.:2.00   3rd Qu.:30.10   3rd Qu.:1.00   3rd Qu.:229.0  
# Max.   :3.00   Max.   :35.30   Max.   :2.00   Max.   :284.0

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