Descriptive stats for MI data in R: Take 3 - r

Question

As an R beginner, I have found it surprisingly difficult to figure out how to compute descriptive statistics on multiply imputed data (more so than running some of the other basic analyses, such as correlations and regressions).
These types of questions are prefaced with apologies (Descriptive statistics (Means, StdDevs) using multiply imputed data: R) but have have not been answered (https://stats.stackexchange.com/questions/296193/pooling-basic-descriptives-from-several-multiply-imputed-datasets-using-mice) or are quickly cast a down vote.
Here is a description of a miceadds function(https://www.rdocumentation.org/packages/miceadds/versions/2.10-14/topics/stats0), which I find difficult to follow with data that has been stored in the mids format.
I have gotten some output such as mean, median, min, max using the summary(complete(imp)) but would love to know how to get additional summary output (e.g., skew/kurtosis, standard deviation, variance).
Illustration borrowed from a previous poster above:
> 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
Would someone kindly take the time to illustrate how one might take the mids object to get the basic descriptives?

Answer 1

Below are some steps you can do to better understand what happens with R objects after each step. I would also recommend to look at this tutorial:
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

Answer 2

There are several mistakes in both your code and the answer from Katia and the link provided by Katia is no longer available.
To compute simple statistics after multiple imputation, you must follow Rubin's Rule, which is the method used in mice for a selected bunch of model fits.
When using
library(mice)
imp <- mice(nhanes, seed = 23109)
mat <- complete(imp)
mat
age bmi hyp chl
1 1 28.7 1 199
2 2 22.7 1 187
3 1 30.1 1 187
4 3 22.7 2 204
5 1 20.4 1 113
6 3 24.9 2 184
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 30.1 1 229
11 1 35.3 1 187
12 2 27.5 1 229
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 238
16 1 29.6 1 238
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 206
21 1 33.2 1 238
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 284
25 2 27.4 1 186
You only return the first imputed dataset, whereas you imputed five by default. See ?mice::complete for more informations "The default is action = 1L returns the first imputed data set."
To get the five imputed datasets, you have to specify the action argument of mice::complete
mat2 <- complete(imp, "long")
mat2
.imp .id age bmi hyp chl
1 1 1 1 28.7 1 199
2 1 2 2 22.7 1 187
3 1 3 1 30.1 1 187
4 1 4 3 22.7 2 204
5 1 5 1 20.4 1 113
6 1 6 3 24.9 2 184
7 1 7 1 22.5 1 118
8 1 8 1 30.1 1 187
9 1 9 2 22.0 1 238
10 1 10 2 30.1 1 229
...
115 5 15 1 29.6 1 187
116 5 16 1 25.5 1 187
117 5 17 3 27.2 2 284
118 5 18 2 26.3 2 199
119 5 19 1 35.3 1 218
120 5 20 3 25.5 2 218
121 5 21 1 22.7 1 238
122 5 22 1 33.2 1 229
123 5 23 1 27.5 1 131
124 5 24 3 24.9 1 186
125 5 25 2 27.4 1 186
Both summary(mat) and summary(mat2) are false.
Let's focus on bmi. The first one provides the mean bmi over the first imputed dataset. The second one provides the mean of an artifical m times larger dataset. The second dataset also has inappropriately low variance.
mean(mat$bmi)
27.484
mean(mat2$bmi)
26.5192
I have not found a better solution than applying manually Rubin's rule to the mean estimate. The correct estimate is simply the mean of estimates accross all imputed datasets
res <- with(imp, mean(bmi)) #get the mean for each imputed dataset, stored in res$analyses
do.call(sum, res$analyses) / 5 #compute mean over m = 5 mean estimations
26.5192
The variance / standard deviation has to be calculated appropriately. You can use Rubin's rule to compute any simple statistic that you wish. You can find the way of doing so here https://bookdown.org/mwheymans/bookmi/rubins-rules.html
Hope this helps.