rbind.mids {mice} | R Documentation |
Append mids
objects by rows
rbind.mids(x,y,...)
x |
A mids object. |
y |
A mids object or a dataframe, matrix, factor or vector. |
... |
Dataframes, matrices, vectors or factors. These can be given as named arguments. |
This function combines two mids
objects rowwise into a
single mids
object or combines a mids
object and a vector, matrix, factor or dataframe
rowwise into a mids
object. The number of columns in the (incomplete)
data x$data
and y
(or y$data
if y
is a mids
object)
should be equal. If y
is a mids
object then the number of imputations
in x
and y
should be equal.
call |
A vector, with first argument the mice() statement that
created x and second argument the call to rbind.mids() |
data |
The rowwise combination of the (incomplete) data in x and y . |
m |
x$m |
nmis |
An array containing the number of missing observations per column,
defined as x$nmis + y$nmis |
imp |
A list of nvar components with the generated multiple imputations.
Each part of the list is a nmis[j] by m matrix of imputed values for
variable j . If y is a mids object then imp[[j]] equals
rbind(x$imp[[j]], y$imp[[j]]) ; otherwise the original
data of y will be copied into this list, including the missing values of y
then y is not imputed. |
method |
A vector of strings of length(nvar) specifying the elementary
imputation method per column defined as x$method |
predictorMatrix |
A square matrix of size ncol(data) containing code 0/1 data specifying
the predictor set defined as x$predictorMatrix |
visitSequence |
The sequence in which columns are visited, defined as x$visitSequence . |
seed |
The seed value of the solution, x$seed |
iteration |
Last Gibbs sampling iteration number, x$iteration |
lastSeedValue |
The most recent seed value, x$lastSeedValue |
chainMean |
Set to NA |
chainVar |
Set to NA |
pad |
x$pad , a list containing various settings of the padded imputation model,
i.e. the imputation model after creating dummy variables |
Karin Groothuis-Oudshoorn, Stef van Buuren, 2009
Van Buuren, S., Groothuis-Oudshoorn, K. (2009) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf