Skip to contents

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.

Usage

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:

dataColumnwise combination of the data in x and y
impCombines the imputed values from x and y
mTaken from x$m
whereColumnwise combination of x$where and y$where
blocksCombines x$blocks and y$blocks
callVector, call[1] creates x, call[2] is call to cbind.mids
nmisEquals c(x$nmis, y$nmis)
methodCombines x$method and y$method
predictorMatrixCombination with zeroes on the off-diagonal blocks
visitSequenceCombined as c(x$visitSequence, y$visitSequence)
formulasCombined as c(x$formulas, y$formulas)
postCombined as c(x$post, y$post)
blotsCombined as c(x$blots, y$blots)
ignoreTaken from x$ignore
seedTaken from x$seed
iterationTaken from x$iteration
lastSeedValueTaken from x$lastSeedValue
chainMeanCombined from x$chainMean and y$chainMean
chainVarCombined from x$chainVar and y$chainVar
loggedEventsTaken from x$loggedEvents
versionCurrent package version
dateCurrent date

See also

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 30.1   1  NA
#> 2   2 22.7   1 187
#> 3   1 22.5   1 187
#> 4   3 27.4   1  NA
#> 5   1 20.4   1 113
#> 6   3 27.4   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 30.1   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 27.4   1 60-99    NA  <NA>  NA
#> 5   1 20.4   1 20-39  20.4    no 113
#> 6   3 27.4   1 60-99    NA  <NA> 184