Combine mids objects by columns — cbind.mids • mice

This function combines two mids objects columnwise into a single object of class mids, or combines a single mids object with a vector, matrix, factor or data.frame columnwise into a mids object.

cbind.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

Pre-requisites: If y is a mids-object, the rows of x$data and y$data should match, as well as the number of imputations (m). Other y are transformed into a data.frame whose rows should match with x$data.

The function renames any duplicated variable or block names by appending ".1", ".2" to duplicated names.

Note

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

`data`	Columnwise combination of the data in `x` and `y`
`imp`	Combines the imputed values from `x` and `y`
`m`	Taken from `x$m`
`where`	Columnwise combination of `x$where` and `y$where`
`blocks`	Combines `x$blocks` and `y$blocks`
`call`	Vector, `call[1]` creates `x`, `call[2]` is call to `cbind.mids`
`nmis`	Equals `c(x$nmis, y$nmis)`
`method`	Combines `x$method` and `y$method`
`predictorMatrix`	Combination with zeroes on the off-diagonal blocks
`visitSequence`	Combined as `c(x$visitSequence, y$visitSequence)`
`formulas`	Combined as `c(x$formulas, y$formulas)`
`post`	Combined as `c(x$post, y$post)`
`blots`	Combined as `c(x$blots, y$blots)`
`ignore`	Taken from `x$ignore`
`seed`	Taken from `x$seed`
`iteration`	Taken from `x$iteration`
`lastSeedValue`	Taken from `x$lastSeedValue`
`chainMean`	Combined from `x$chainMean` and `y$chainMean`
`chainVar`	Combined from `x$chainVar` and `y$chainVar`
`loggedEvents`	Taken from `x$loggedEvents`
`version`	Current package version
`date`	Current date

Author

Karin Groothuis-Oudshoorn, Stef van Buuren

Examples


# impute four variables at once (default)
imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE)
imp$predictorMatrix
#>     age bmi hyp chl
#> age   0   1   1   1
#> bmi   1   0   1   1
#> hyp   1   1   0   1
#> chl   1   1   1   0

# impute two by two
data1 <- nhanes[, c("age", "bmi")]
data2 <- nhanes[, c("hyp", "chl")]
imp1 <- mice(data1, m = 2, maxit = 1, print = FALSE)
imp2 <- mice(data2, m = 2, maxit = 1, print = FALSE)

# Append two solutions
imp12 <- cbind(imp1, imp2)

# This is a different imputation model
imp12$predictorMatrix
#>     age bmi hyp chl
#> age   0   1   0   0
#> bmi   1   0   0   0
#> hyp   0   0   0   1
#> chl   0   0   1   0

# Append the other way around
imp21 <- cbind(imp2, imp1)
imp21$predictorMatrix
#>     hyp chl age bmi
#> hyp   0   1   0   0
#> chl   1   0   0   0
#> age   0   0   0   1
#> bmi   0   0   1   0

# Append 'forgotten' variable chl
data3 <- nhanes[, 1:3]
imp3 <- mice(data3, maxit = 1, m = 2, print = FALSE)
imp4 <- cbind(imp3, chl = nhanes$chl)

# Of course, chl was not imputed
head(complete(imp4))
#>   age  bmi hyp chl
#> 1   1 26.3   1  NA
#> 2   2 22.7   1 187
#> 3   1 22.5   1 187
#> 4   3 21.7   1  NA
#> 5   1 20.4   1 113
#> 6   3 22.7   1 184

# Combine mids object with data frame
imp5 <- cbind(imp3, nhanes2)
head(complete(imp5))
#>   age  bmi hyp age.1 bmi.1 hyp.1 chl
#> 1   1 26.3   1 20-39    NA  <NA>  NA
#> 2   2 22.7   1 40-59  22.7    no 187
#> 3   1 22.5   1 20-39    NA    no 187
#> 4   3 21.7   1 60-99    NA  <NA>  NA
#> 5   1 20.4   1 20-39  20.4    no 113
#> 6   3 22.7   1 60-99    NA  <NA> 184