`mids`

)The `mids`

object contains a multiply imputed data set. The `mids`

object is
generated by the `mice()`

and `mice.mids()`

functions. The `mids`

class of objects has methods for the following generic functions:
`print`

, `summary`

, `plot`

.

`.Data`

:Object of class

`"list"`

containing the following slots:`call`

:The call that created the object.

`data`

:A copy of the incomplete data set.

`m`

:The number of imputations.

`nmis`

:An array containing the number of missing observations per column.

`imp`

:A list of

`ncol(data)`

components with the generated multiple imputations. Each part of the list is a`nmis[j]`

by`m`

matrix of imputed values for variable`j`

.`method`

:A vector of strings of

`length(ncol(data))`

specifying the elementary imputation method per column.`predictorMatrix`

:A square matrix of size

`ncol(data)`

containing integers specifying the predictor set.`visitSequence`

:The sequence in which columns are visited.

`post`

:A vector of strings of length

`ncol(data)`

with commands for post-processing`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 six columns containing warnings, corrective actions, and other inside info.`pad`

:A list containing various settings of the padded imputation model, i.e. the imputation model after creating dummy variables. Normally, this list is only useful for error checking. List members are

`pad$data`

(data padded with columns for factors),`pad$predictorMatrix`

(predictor matrix for the padded data),`pad$method`

(imputation methods applied to the padded data), the vector`pad$visitSequence`

(the visit sequence applied to the padded data),`pad$post`

(post-processing commands for padded data) and`categories`

(a matrix containing descriptive information about the padding operation).`loggedEvents`

:A matrix with six 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, and 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), and 2. proportional odds regression imputation that does not converge and is replaced by`polyreg`

. Column`it`

is the iteration number at which the record was added,`im`

is the imputation number,`co`

is the column number in the data,`dep`

is the name of the name of the dependent variable,`meth`

is the imputation method used, and`out`

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

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

:
Multivariate Imputation by Chained Equations in `R`

. *Journal of
Statistical Software*, **45**(3), 1-67.
http://www.jstatsoft.org/v45/i03/