mids object contains a multiply imputed data set. The
mids object is
generated by functions
Object of class
"list" containing the
Original (incomplete) data set.
A list of
ncol(data) components with
the generated multiple imputations. Each list components is a
m) of imputed values
Number of imputations.
where argument of the
blocks argument of the
Call that created the object.
An array containing the number of missing observations per column.
A vector of strings of
specifying the imputation method per block.
A numerical matrix of containing integers specifying the predictor set.
The sequence in which columns are visited.
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.
A vector of strings of length
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
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 five columns
containing warnings, corrective actions, and other inside info.
Version number of
mice package that
created the object.
Date at which the object was created.
class of objects has methods for the following generic functions:
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:
A variable that contains missing values, that is not imputed and that is used as a predictor is removed
A constant variable is removed
A collinear variable is removed.
During iteration, the program does the following actions:
One or more variables that are linearly dependent are removed (for categorical data, a 'variable' corresponds to a dummy variable)
Proportional odds regression imputation that does not converge
and is replaced by
Explanation of elements in
iteration number at which the record was added,
name of the dependent variable,
imputation method used,
a (possibly long) character vector with the names of the altered or removed predictors.
van Buuren S and Groothuis-Oudshoorn K (2011).
Multivariate Imputation by Chained Equations in
R. Journal of
Statistical Software, 45(3), 1-67.