mice: rbind.mids – R documentation

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rbind.mids

Combine mids objects by rows

Description

This function combines two mids objects rowwise into a single mids object, or combines a mids object with a vector, matrix, factor or dataframe rowwise into a mids object.

Usage

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

Arguments

`x`	A `mids` object.
`y`	A `mids` object, or a `data.frame`, `matrix`, `factor` or `vector`.
`...`	Additional `data.frame`, `matrix`, `vector` or `factor`. These can be given as named arguments.

Details

If y is a mids object, then rbind requires that the number of multiple imputations in x and y is identical. Also, columns of x$data and y$data should match.

If y is not a mids object, the columns of x$data and y should match. The where matrix for y is set to FALSE, signaling that any missing values in y were not imputed. The ignore vector for y is set to FALSE, elements of y will therefore influence the parameters of the imputation model in future iterations.

Value

An S3 object of class mids

Note

The function construct the elements of the new mids object as follows:

`data`	Rowwise combination of the (incomplete) data in `x` and `y`
`imp`	Equals `rbind(x$imp[[j]], y$imp[[j]])` if `y` is `mids` object; otherwise the data of `y` will be copied
`m`	Equals `x$m`
`where`	Rowwise combination of `where` arguments
`blocks`	Equals `x$blocks`
`call`	Vector, `call[1]` creates `x`, `call[2]` is call to `rbind.mids`
`nmis`	`x$nmis` + `y$nmis`
`method`	Taken from `x$method`
`predictorMatrix`	Taken from `x$predictorMatrix`
`visitSequence`	Taken from `x$visitSequence`
`formulas`	Taken from `x$formulas`
`post`	Taken from `x$post`
`blots`	Taken from `x$blots`
`ignore`	Concatenate `x$ignore` and `y$ignore`
`seed`	Taken from `x$seed`
`iteration`	Taken from `x$iteration`
`lastSeedValue`	Taken from `x$lastSeedValue`
`chainMean`	Set to `NA`
`chainVar`	Set to `NA`
`loggedEvents`	Taken from `x$loggedEvents`
`version`	Taken from `x$version`
`date`	Taken from `x$date`

Author(s)

Karin Groothuis-Oudshoorn, Stef van Buuren

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. https://www.jstatsoft.org/v45/i03/

Examples

imp1 <- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE)
imp5 <- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE)
mylist <- list(age = NA, bmi = NA, hyp = NA, chl = NA)

nrow(complete(rbind(imp1, imp5)))
nrow(complete(rbind(imp1, mylist)))

nrow(complete(rbind(imp1, data.frame(mylist))))
nrow(complete(rbind(imp1, complete(imp5))))

mice

Multivariate Imputation by Chained Equations

v3.13.0

GPL-2 | GPL-3

Authors

Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb]

Initial release

2021-01-26