使用MICE包插入后创建新变量

Question

I have longitudinal panel data of 1000 individuals measured at two time points. Using the MICE package I have imputed values for those variables with missing data. The imputation itself works fine, generating the required 17 imputed data frames. One of the imputed variables is fitness . I would like to create a new variable of fitness scaled, scale(fitness) . My understanding is that I should impute first, and then create the new variable with the imputed data. How do I access each of the 17 imputed datasets and generate a scaled fitness variable in each?

My original data frame looks like (some variables missing):

      id   age school   sex      andersen ldl_c_trad  pre_post
   <dbl> <dbl>  <fct>  <fct>        <int>      <dbl>     <fct>
 1     2  10.7      1      1          951       2.31         1
 2     2  11.3      1      1          877       2.20         2
 3     3  11.3      1      1          736       2.88         1
 4     3  11.9      1      1          668       3.36         2
 5     4  10.1      1      0          872       3.31         1
 6     4  10.7      1      0          905       2.95         2
 7     5  10.5      1      1          925       2.02         1
 8     5  11.0      1      1          860       1.92         2
 9     8  10.7      1      1          767       3.41         1
10     8  11.2      1      1          709       3.32         2

My imputation code is:

imputed <- mice(imp_vars, method = meth, predictorMatrix = predM, m = 17)

imp_vars are the variables selected for imputation. I have pre-specified both the method and predictor matrix.

Also, my assumption is that the scaling should be performed separately for each time point, as fitness is likely to have improved over time. Is it possible to perform the scaling filtered by pre_post for each imputed dataset?

Many thanks.

Answer 1

To access each of the imputations where x is a value from 1-17

data <- complete(imputed, x)

or if you want access to the fitness variable

complete(imputed, x)$fitness

If you want to filter observations according to a value of another variable in the dataframe, you could use

data[which(data$pre_post==1), "fitness"]

This should return the fitness observations for when pre_post==1, from there it is simply a matter of scaling these observations for each level of pre_post, assigning them to another variable fitness_scaled and then repeating for each imputation 1-17.