cbind: Combine R objects by rows and columns

In mice: Multivariate Imputation by Chained Equations

Combine R objects by rows and columns

Description

Functions cbind() and rbind() are defined in the mice package in order to enable dispatch to cbind.mids() and rbind.mids() when one of the arguments is a data.frame.

Usage

cbind(...)

rbind(...)

Arguments

...

Arguments passed on to base::cbind deparse.level integer controlling the construction of labels in the case of non-matrix-like arguments (for the default method): deparse.level = 0 constructs no labels; the default deparse.level = 1 typically and deparse.level = 2 always construct labels from the argument names, see the ‘Value’ section below.

Details

The standard base::cbind() and base::rbind() always dispatch to base::cbind.data.frame() or base::rbind.data.frame() if one of the arguments is a data.frame. The versions defined in the mice package intercept the user command and test whether the first argument has class "mids". If so, function calls cbind.mids(), respectively rbind.mids(). In all other cases, the call is forwarded to standard functions in the base package.

The cbind.mids() 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.

If both arguments of cbind.mids() are mids-objects, the data list components should have the same number of rows. Also, the number of imputations (m) should be identical. If the second argument is a matrix, factor or vector, it is transformed into a data.frame. The number of rows should match with the data component of the first argument.

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

The rbind.mids() function combines two mids objects rowwise into a single mids object, or combines a mids object with a vector, matrix, factor or data frame rowwise into a mids object.

If both arguments of rbind.mids() are mids objects, then rbind.mids() requires that both have the same number of multiple imputations. In addition, their data components should match.

If the second argument of rbind.mids() is not a mids object, the columns of the arguments should match. The where matrix for the second argument is set to FALSE, signalling that any missing values in that argument were not imputed. The ignore vector for the second argument is set to FALSE. Rows inherited from the second argument will therefore influence the parameter estimation of the imputation model in any future iterations.

Value

An S3 object of class mids

Note

The cbind.mids() 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

The rbind.mids() function constructs 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v045.i03")}

See Also

cbind, ibind, mids

Examples

# --- cbind ---
# impute four variables at once (default)
imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE)
imp$predictorMatrix

# 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

# Append the other way around
imp21 <- cbind(imp2, imp1)
imp21$predictorMatrix

# 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))

# Combine mids object with data frame
imp5 <- cbind(imp3, nhanes2)
head(complete(imp5))

# --- rbind ---
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 documentation built on June 8, 2025, 11:31 a.m.