complete {mice} | R Documentation |
mids
objectTakes an object of class mids
, fills in the missing data, and returns
the completed data in a specified format.
complete(x, action = 1, include = FALSE)
x |
An object of class |
action |
If action is a scalar between 1 and |
include |
Flag to indicate whether the orginal data with the missing
values should be included. This requires that |
The argument action
can also be a string, which is partially matched
as follows:
produces a long data frame of
vertically stacked imputed data sets with nrow(x$data)
* x$m
rows and ncol(x$data)+2
columns. The two additional columns are
labeled .id
containing the row names of x$data
, and .imp
containing the imputation number. If include=TRUE
then
nrow(x$data)
additional rows with the original data are appended with
.imp
set equal to 0
.
produces a broad data frame with
nrow(x$data)
rows and ncol(x$data)
* x$m
columns.
Columns are ordered such that the first ncol(x$data)
columns
corresponds to the first imputed data matrix. The imputation number is
appended to each column name. If include=TRUE
then
ncol(x$data)
additional columns with the original data are appended.
The number .0
is appended to the column names.
produces a broad data frame with
nrow(x$data)
rows and ncol(x$data)
* x$m
columns.
Columns are ordered such that the first x$m
columns correspond to the
x$m
imputed versions of the first column in x$data
. The
imputation number is appended to each column name. If include=TRUE
then ncol(x$data)
additional columns with the original data are
appended. The number .0
is appended to the column names.
A data frame with the imputed values filled in. Optionally, the original data are appended.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2009
# do default multiple imputation on a numeric matrix imp <- mice(nhanes) # obtain first imputated matrix mat <- complete(imp) # fill in the third imputation mat <- complete(imp, 3) # long matrix with stacked complete data mat <- complete(imp, 'long') # long matrix with stacked complete data, including the original data mat <- complete(imp, 'long', inc=TRUE) # repeated matrix with complete data mat <- complete(imp, 'r') # for numeric data, produces a blocked correlation matrix, where # each block contains of the same variable pair over different # multiple imputations. cor(mat)