Combine mids objects by columns
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, ...)
x |
A |
y |
A |
... |
Additional |
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$data.
The function renames any duplicated variable or block names by
appending ".1", ".2" to duplicated names.
An S3 object of class mids
The function constructs the elements of the new mids object as follows:
data |
Columnwise combination of the data in x and y
|
imp |
Combines the imputed values from x and y
|
m |
Taken from x$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$loggedEvents
|
version |
Current package version |
date |
Current date |
Karin Groothuis-Oudshoorn, Stef van Buuren
cbind, rbind.mids, ibind,
mids
# impute four variables at once (default)
imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE)
imp$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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.