Multiply imputed data set (
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.
mice package does not use
the S4 class definitions, and instead relies on the S3 list
oldClass(obj) <- "mids".
Object of class
"list"containing the following slots:
Original (incomplete) data set.
A list of
ncol(data)components with the generated multiple imputations. Each list component is a
m) of imputed values for variable
NULLcomponent is used for variables for which not imputations are generated.
Number of imputations.
whereargument of the
blocksargument of the
Call that created the object.
An array containing the number of missing observations per column.
A vector of strings of
length(blocksspecifying the imputation method per block.
A numerical matrix of containing integers specifying the predictor set.
A vector of variable and block names that specifies how variables and blocks are visited in one iteration throuh the data.
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
length(blocks)with commands for post-processing.
"Block dots". The
blotsargument to the
A logical vector of length
nrow(data)indicating the rows in
dataused to build the imputation model. (new in
The seed value of the solution.
Last Gibbs sampling iteration number.
The most recent seed value.
An array of dimensions
melements 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.
An array with similar structure as
chainMean, containing the variance of the imputed values.
data.framewith five columns containing warnings, corrective actions, and other inside info.
Version number of
micepackage that created the object.
Date at which the object was created.
van Buuren S and Groothuis-Oudshoorn K (2011).
Multivariate Imputation by Chained Equations in
R. Journal of
Statistical Software, 45(3), 1-67.