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.
blots
:"Block dots". The blots
argument to the mice()
function.
ignore
:A logical vector of length nrow(data)
indicating
the rows in data
used to build the imputation model. (new in mice 3.12.0
)
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.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
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
:
it
iteration number at which the record was added,
im
imputation number,
dep
name of the dependent variable,
meth
imputation method used,
out
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.
tools:::Rd_expr_doi("10.18637/jss.v045.i03")
mice
, mira
,
mipo