mice (version 2.25)

mids-class: Multiply imputed data set (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.



Object of class "list" containing the following slots:
The call that created the object.
A copy of the incomplete data set.
The number of imputations.
An array containing the number of missing observations per column.
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.
A vector of strings of length(ncol(data)) specifying the elementary imputation method per column.
A square matrix of size ncol(data) containing integers specifying the predictor set.
The sequence in which columns are visited.
A vector of strings of length ncol(data) with commands for post-processing
The seed value of the solution.
Last Gibbs sampling iteration number.
The most recent seed value.
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.
A list with similar structure of chainMean, containing the covariances of the imputed values.
A data.frame with six columns containing warnings, corrective actions, and other inside info.
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).
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/

See Also

mice, mira, mipo