This question is related to how to get a complete data set from one containing missing values, and how to impute new cases.

The mice R package impute missing values. Its algorithm produces M completed data sets, where all non-empty values remaing the same, but the empty are reeplaced based on a set of conditional densities based on certain distributions.

The doubt is: If it is needed to create some predictive model, is it valid to create the training data by averaging all values across M data sets, so the non-empty values will remain the same and the imputed cases will be averaged? That is for numeric variables, for categorical the mode can be used.

Other approach could be to append all cases to a "big" data set, and train the model with this set.

Finally once the model is running live on production, new cases will be imputed with the same criteria.

Does it makes sense?

The mice paper can be found at: https://www.jstatsoft.org/index.php/jss/article/view/v045i03/v45i03.pdf


# do default multiple imputation on a numeric matrix, 5 imputation data frames
imp <- mice(nhanes, m = 5)

# get a final data set containing the 5 imputed data frames, total rows=nrow(nhanes)*5
data_all <- complete(imp, "long")

# data_all contains the same columns as nhanes plus 2 more: '.id' and '.imp'
# .id=row number, from 1 to 25
# .imp=imputation data frame id, 1 to 5 ('m' parameter)

The grouping can be done using .id and .imp variables, or just use the final data as it is: data_all.


1 Answer 1


You should run the model on all the imputed datasets separately and then pool the resulting regression estimates. See the article you provided MICE: Multivariate Imputation by Chained Equations in R (especially chapter 5 and point 5.3) by Buuren & Groothuis-Oudshoorn, 2011. Then you can use the pooled model on the training data to test the model.

The problem with averaging imputed data is that you will omit some variability in the data. The problem with a second proposition is that you will artificially inflate the sample size. This will render error estimates of your predictions based on the model useless.

EXT: regression model used on the imputed data should be a nested model of a regression model used to impute data. This and pooling methods provided implies that MICE method was designed with regression analysis methods in mind. But there is no reason not to use other methods on imputed data.

The problem is how to generalize the models obtained on many imputed datasets. This step depends on the method used. In a case of tree based models you can simply append all the imputed data into one big data frame (as you proposed). The resulting tree model will in essence be the average pooled tree model.

  • $\begingroup$ I know about that and agreed with you. The problem with the pool() function is the limitation regarding the models it can manage. How about creating a random forest or gradient boosting machine with the imputed data? pool averages the parameters of only a bunch of models, perhaps the answer could be: "it is not possible to use mice with other models than the specified" $\endgroup$ Commented Apr 17, 2017 at 17:39
  • $\begingroup$ Okay, I appended my answer based on your comment. Kind regards! $\endgroup$
    – Vivaldi
    Commented Apr 17, 2017 at 18:21
  • $\begingroup$ @pablo_sci do you have any aditional comments? Otherwise please let me know if answer satisfies what you were asking for. $\endgroup$
    – Vivaldi
    Commented Apr 21, 2017 at 14:02
  • $\begingroup$ Thanks. I don't understand this: "The resulting tree model will in essence be the average pooled tree model.". Also I want to wait if any other person comment with other opinion, or, your answer is more upvoted. I'd like to have something more technical or other comments coming from the experience (like your answer). $\endgroup$ Commented Apr 21, 2017 at 18:03
  • $\begingroup$ I like the idea of averaging the tree model, so I will mark the question as solved. Would be nice to hear more opinions on this approach. Thanks @Vivaldi. $\endgroup$ Commented May 9, 2017 at 1:23

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