cbind.mids: Combine 'mids' objects by columns

In mice: Multivariate Imputation by Chained Equations

Description

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.

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.

Value

An S3 object of class mids

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(s)

Karin Groothuis-Oudshoorn, Stef van Buuren

See Also

cbind, rbind.mids, ibind, mids

Examples

# 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))

mice documentation built on Nov. 14, 2020, 5:07 p.m.