Combine mids objects by rows — rbind.mids • mice

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.

Value

An S3 object of class mids

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.

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`

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. doi:10.18637/jss.v045.i03

Author

Karin Groothuis-Oudshoorn, Stef van Buuren

Examples

imp1 <- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE)
#> Warning: Number of logged events: 1
imp5 <- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE)
#> Warning: Number of logged events: 1
mylist <- list(age = NA, bmi = NA, hyp = NA, chl = NA)

nrow(complete(rbind(imp1, imp5)))
#> Warning: iterations differ, so no convergence diagnostics calculated
#> [1] 26
nrow(complete(rbind(imp1, mylist)))
#> [1] 14

nrow(complete(rbind(imp1, data.frame(mylist))))
#> [1] 14
nrow(complete(rbind(imp1, complete(imp5))))
#> [1] 26

Combine `mids` objects by rows