rbind.mids {mice}R Documentation

Combine a Multiply Imputed Data Set with other mids object or dataframe

Description

Append mids objects by rows

Usage

rbind.mids(x,y,...)

Arguments

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.

Details

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.

Value

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

Author(s)

Karin Groothuis-Oudshoorn, Stef van Buuren, 2009

References

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/

See Also

cbind.mids, ibind, mids


[Package mice version 2.11 Index]