| rbind.mids {mice} | R Documentation |
mids object.Append mids objects by rows
rbind.mids(x, y, ...)
x |
A |
y |
A |
... |
Additional |
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.
An S3 object of class mids
Component call is a vector, with first argument the mice() statement
that created x and second argument the call to rbind.mids(). Component
data is the rowwise combination of the (incomplete) data in x
and y.
Component m is equal to x$m.
Component nmis is an array containing the number of missing observations per
column, defined as x$nmis + y$nmis.
Component imp is a list of nvar components with the generated multiple
imputations. Each part of the list is a nmis[j] by m matrix of
imputed values for variable j. If y is a mids object
then imp[[j]] equals rbind(x$imp[[j]], y$imp[[j]]); otherwise
the original data of y will be copied into this list, including the
missing values of y then y is not imputed.
Component method is a vector of strings of length(nvar) specifying the
elementary imputation method per column defined as x$method.
Component predictorMatrix is a square matrix of size ncol(data)
containing the predictor set defined as
x$predictorMatrix.
Component visitSequence is the sequence in which columns are visited, defined
as x$visitSequence.
Component seed is the seed value of the solution, x$seed.
Component iteration is the last Gibbs sampling iteration number,
x$iteration.
Component lastSeedValue is the most recent seed value, x$lastSeedValue
Component chainMean is set to NA.
Component chainVar is set to NA.
Component pad is set to x$pad, a list containing various settings of the
padded imputation model, i.e. the imputation model after creating dummy
variables.
Component loggedEvents is set to x$loggedEvents.
Karin Groothuis-Oudshoorn, Stef van Buuren, 2009
van Buuren S and Groothuis-Oudshoorn K (2011). mice:
Multivariate Imputation by Chained Equations in R. Journal of
Statistical Software, 45(3), 1-67.
http://www.jstatsoft.org/v45/i03/