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MICE is an R package which implements Multivariate Imputation by Chained Equations using Fully Conditional Specification

2 votes

Training set does not have missing values, but test set does. How to handle?

The first two should be scenarios that a suitable form of e.g. multiple imputation can address (not sure I'd use MICE, because the chaining of the equations tends to induce annoying limitations, but e.g …
Björn's user avatar
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1 vote

MAR Assumption missing data

If you are wondering how many records to impute per imputation sample, then the answer would be as many as there would have been without missing data. If the question is on the number of imputed datas …
Björn's user avatar
  • 35.3k
1 vote
Accepted

Missing data in predictors, covariates and outcomes: can i impute them all together?

If your assumptions in your imputation model are exactly met (usually a MAR or some well-specified modification of it), then you can do multiple imputation for everything together. However, people are …
Björn's user avatar
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4 votes
Accepted

How to select the best dataset after multiple imputation in MICE to build other models

You should fit your model to each of the multiple imputations and then combine the results (e.g. using Rubin's rule). That way the uncertainty about your final analysis result does not just come from …
Björn's user avatar
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2 votes
Accepted

Iterations in Multiple Imputation

The idea of multiple imputation is to create multiple imputed datasets, for which the missing values are replaced by imputed values that differ across the multiple imputed datasets. The variation in t …
Björn's user avatar
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3 votes

Can you impute (predict) missing continuous data using categorical data as the predictor?

You may also want to check whether MI via chained equations (MICE) is the most appropriate approach for your situation (for some reason it's very popular, but tends to perform poorly in quite a few simulations …
Björn's user avatar
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1 vote

How to handle missing data in the univariate analysis

In general, an imputation model should be as complex or more complex than the analysis model. I.e. just because you look at one variable in the analysis does not mean you would restrict yourself to th …
Björn's user avatar
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7 votes
Accepted

Stepwise regression modeling using multiply imputed data sets

Avoid stepwise regression due to the many issues indicated in many other threads. If you search Google.Scholar for methods that are more appropriate for variable selection (e.g. cross-validation app …
Björn's user avatar
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