R: Multiply imputed data set ('mids')

mids-class {mice} | R Documentation

## Multiply imputed data set (`mids`)

### Description

The `mids` object contains a multiply imputed data set. The `mids` object is generated by functions `mice()`, `mice.mids()`, `cbind.mids()`, `rbind.mids()` and `ibind.mids()`.

### Details

The `mids` class of objects has methods for the following generic functions: `print`, `summary`, `plot`.

The `loggedEvents` entry is a matrix with five columns containing a record of automatic removal actions. It is `NULL` is no action was made. At initialization the program does the following three actions:

1

  A variable that contains missing values, that is not imputed and that is used as a predictor is removed

2

  A constant variable is removed

3

  A collinear variable is removed.

During iteration, the program does the following actions:

1

  One or more variables that are linearly dependent are removed (for categorical data, a 'variable' corresponds to a dummy variable)

2

  Proportional odds regression imputation that does not converge and is replaced by `polyreg`.

Explanation of elements in `loggedEvents`:

`it`

  iteration number at which the record was added,

`im`

  imputation number,

`dep`

  name of the dependent variable,

`meth`

  imputation method used,

`out`

  a (possibly long) character vector with the names of the altered or removed predictors.

### Slots

`.Data`:

  Object of class `"list"` containing the following slots:

`data`:

  Original (incomplete) data set.

`imp`:

  A list of `ncol(data)` components with the generated multiple imputations. Each list components is a `data.frame` (`nmis[j]` by `m`) of imputed values for variable `j`.

`m`:

  Number of imputations.

`where`:

  The `where` argument of the `mice()` function.

`blocks`:

  The `blocks` argument of the `mice()` function.

`call`:

  Call that created the object.

`nmis`:

  An array containing the number of missing observations per column.

`method`:

  A vector of strings of `length(blocks` specifying the imputation method per block.

`predictorMatrix`:

  A numerical matrix of containing integers specifying the predictor set.

`visitSequence`:

  The sequence in which columns are visited.

`formulas`:

  A named list of formula's, or expressions that can be converted into formula's by `as.formula`. List elements correspond to blocks. The block to which the list element applies is identified by its name, so list names must correspond to block names.

`post`:

  A vector of strings of length `length(blocks)` with commands for post-processing.

`blots`:

  "Block dots". The `blots` argument to the `mice()` function.

`ignore`:

  A logical vector of length `nrow(data)` indicating the rows in `data` used to build the imputation model. (new in `mice 3.12.0`)

`seed`:

  The seed value of the solution.

`iteration`:

  Last Gibbs sampling iteration number.

`lastSeedValue`:

  The most recent seed value.

`chainMean`:

  A list of `m` components. Each component is a `length(visitSequence)` by `maxit` matrix containing the mean of the generated multiple imputations. The array can be used for monitoring convergence. Note that observed data are not present in this mean.

`chainVar`:

  A list with similar structure of `chainMean`, containing the covariances of the imputed values.

`loggedEvents`:

  A `data.frame` with five columns containing warnings, corrective actions, and other inside info.

`version`:

  Version number of `mice` package that created the object.

`date`:

  Date at which the object was created.

### Note

The `mice` package does not use the S4 class definitions, and instead relies on the S3 list equivalent `oldClass(obj) <- "mids"`.

### Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

### References

van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

### See Also

`mice`, `mira`, `mipo`

[Package mice version 3.13.0 Index]