How do mice impute missing values?
To impute the missing values, mice package use an algorithm in a such a way that use information from other variables in dataset to predict and impute the missing values. Therefore, you may not want to use certain variable as predictors. For example the ID variable does not have any predictive value.
Can mice impute factors?
The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation.
How does mice impute missing values in a data set?
To sum up, MICE imputes missing values in the variables of a data set by using a divide and conquer approach – in other words, by focusing on one variable at a time. Once the focus is placed on one variable, MICE uses all the other variables in the data set (or a sensibly chosen subset of these variables) to predict missingness in that variable.
How are multiple imputation by chained equations ( MICE ) used?
I have seen Multiple Imputation by Chained Equations (MICE) used as a missing data handling method – is anyone able to provide a simple explanation of how MICE works?
Which is the correct method for missing variable imputation?
By default, the method uses pmm, predictive mean matching (numeric data) logreg, logistic regression imputation (binary data, factor with 2 levels) polyreg, polytomous regression imputation for unordered categorical data (factor > 2 levels) polr, proportional odds model for (ordered, > 2 levels). Your question isn’t entirely clear to me.
Can a variable be imputed as a predictor?
Therefore, you may not want to use a certain variable as predictors. For example, the ID variable does not have any predictive value. The code below will remove the variable as a predictor but still will be imputed.