CODEWITHSUNDEEP
rimputationr-mice
etc
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Reputation: 15

Exporting multiple imputed objects with MICE

I am imputing my missing data using mice. The problem here is that it takes an hour or two to get the imputation done. So as it finishes imputing, I would like to export it for future use so that I could avoid repeating the time-consuming imputation process in case I have to revisit the analysis.

I have searched on Google and I found a function miceadds::write.mice.imputation. I have looked at the manual. It offered an example of exporting, but I am not sure how to import it back. It seems to have generated some .dat file.

say I have the following code:

# Model 1: Imputation using mice  
imp1 <- mice::mice( nhanes, m=3, maxit=5 )  
# write results 
write.mice.imputation(mi.res=imp1, name="mice_imp1" )

Upvotes: 1

Views: 2134

Answers (2)

hnguyen
hnguyen

Reputation: 814

est_long <- parlmice(nhanes, cluster.seed = 123, print = FALSE, n.core = 4, n.imp.core = 100) %>%
  mice::complete("long")

gives

head(est_long)
  .imp .id age  bmi hyp chl
1    1   1   1 22.0   1 131
2    1   2   2 22.7   1 187
3    1   3   1 27.2   1 187
4    1   4   3 27.2   2 284
5    1   5   1 20.4   1 113
6    1   6   3 20.4   1 184

You can follow up by breaking the long data set into a list of 400 data sets.

    est_long <- parlmice(nhanes, cluster.seed = 123, print = FALSE, n.core = 4, n.imp.core = 100) %>%
      mice::complete("long")%>%
  group_by(.imp) %>% 
  nest()  
head(est_long)
# A tibble: 6 x 2
# Groups:   .imp [6]
   .imp data                 
  <int> <list>               
1     1 <tibble[,5] [25 × 5]>
2     2 <tibble[,5] [25 × 5]>
3     3 <tibble[,5] [25 × 5]>
4     4 <tibble[,5] [25 × 5]>
5     5 <tibble[,5] [25 × 5]>
6     6 <tibble[,5] [25 × 5]>

Upvotes: 0

DJV
DJV

Reputation: 4873

If you noticed when you use write.mice.imputation, its default values are to save your imputed data in various types of files (csv, spss, dat, Rdata).

We can create a sample data: set.seed(1) df <- data.frame(group = sample(c(1:5, NA), replace = TRUE, size = 10), val = sample(c(10:15, NA), replace = TRUE, size = 10))

Load and impute our data: 

require(mice)
require(miceadds)
imp1 <- mice::mice(df, m=3, maxit=5 )

Write our results:

write.mice.imputation(mi.res=imp1, name="mice_imp1", 
                      include.varnames=TRUE,
                      long=TRUE, mids2spss=TRUE,
                      spss.dec=",", dattype=NULL)

Now, we can load what ever file type is comfortable for you. For example, dat file:

oldData <- read.table("mice_imp1/mice_imp1__LONG.dat")

Upvotes: 0

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