mids)The mids object contains a multiply imputed data set. The mids object is
generated by functions mice(), mice.mids(), cbind.mids(),
rbind.mids() and ibind.mids().
.Data:Object of class "list" containing the
following slots:
data:Original (incomplete) data set.
imp:A list of ncol(data) components with
the generated multiple imputations. Each list components is a
data.frame (nmis[j] by m) of imputed values
for variable j.
m:Number of imputations.
where:The where argument of the
mice() function.
blocks:The blocks argument of the
mice() function.
call:Call that created the object.
nmis:An array containing the number of missing observations per column.
method:A vector of strings of length(blocks
specifying the imputation method per block.
predictorMatrix:A numerical matrix of containing integers specifying the predictor set.
visitSequence:The sequence in which columns are visited.
formulas: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.
post:A vector of strings of length length(blocks)
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 five columns
containing warnings, corrective actions, and other inside info.
version:Version number of mice package that
created the object.
date:Date at which the object was created.
The mids
class of objects has methods for the following generic functions:
print, summary, plot.
The 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 polyreg.
Explanation of elements in loggedEvents:
ititeration number at which the record was added,
imimputation number,
depname of the dependent variable,
methimputation method used,
outa (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.
https://www.jstatsoft.org/v45/i03/