`mids`

)`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.

`mice`

:
Multivariate Imputation by Chained Equations in `R`

. `mice`

, `mira`

, `mipo`