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()`

.

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.

The `mice`

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

.

`.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 state.

`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.

van Buuren S and Groothuis-Oudshoorn K (2011). `mice`

:
Multivariate Imputation by Chained Equations in `R`

. *Journal of
Statistical Software*, **45**(3), 1-67.
doi: 10.18637/jss.v045.i03

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000