mids object contains a multiply imputed data set. The
mids object is
generated by the
mice.mids() functions. The
class of objects has methods for the following generic functions:
Object of class
"list" containing the
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
m matrix of
imputed values for variable
A vector of strings of
length(ncol(data)) specifying the
elementary imputation method per column.
A square matrix of size
containing integers specifying the predictor set.
The sequence in which columns are visited.
A vector of strings of length
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
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
containing the covariances of the imputed values.
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
(data padded with columns for factors),
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
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
meth is the imputation method used, and
is a (possibly long) character vector with the names of the altered or
van Buuren S and Groothuis-Oudshoorn K (2011).
Multivariate Imputation by Chained Equations in
R. Journal of
Statistical Software, 45(3), 1-67.