cbind.mids {mice} | R Documentation |
mids
object.This function combines two mids
objects columnwise into a single
object of class mids
, or combines a mids
object with a vector
,
matrix
, factor
or data.frame
columnwise into an object of class mids
.
The number of rows in the (incomplete) data x$data
and y
(or
y$data
if y
is a mids
object) should be equal. If
y
is a mids
object then the number of imputations in x
and y
should be equal. Note: If y
is a vector or factor its
original name is lost and it will be denoted with y
in the mids
object.
cbind.mids(x, y, ...)
x |
A |
y |
A |
... |
Additional |
An S3 object of class mids
Component call
is a vector, with first argument the mice()
statement
that created x
and second argument the call to cbind.mids()
.
Component data
is the codecbind of the (incomplete) data in x$data
and y$data
. Component m
is the number of imputations.
Component nmis
is an array containing the number of missing observations per
column.
Component imp
is a list of nvar
components with the generated multiple
imputations. Each part of the list is a nmis[j]
by m
matrix of
imputed values for variable j
. The original data of y
will be
copied into this list, including the missing values of y
then y
is not imputed.
Component method
is a vector of strings of length(nvar)
specifying the
elementary imputation method per column. If y
is a mids
object this
vector is a combination of x$method
and y$method
, otherwise
this vector is x$method
and for the columns of y
the method is
set to ''
.
Component predictorMatrix
is a square matrix of size ncol(data)
containing integer data specifying the predictor set. If x
and
y
are mids
objects then the predictor matrices of x
and
y
are combined with zero matrices on the off-diagonal blocks.
Otherwise the variables in y
are included in the predictor matrix of
x
such that y
is not used as predictor(s) and not imputed as
well.
Component visitSequence
is the sequence in which columns are visited. The same
as x$visitSequence
.
Component seed
is the seed value of the solution, x$seed
.
Component iteration
is the last Gibbs sampling iteration number,
x$iteration
.
Component lastSeedValue
is the most recent seed value, x$lastSeedValue
Component chainMean
is the combination of x$chainMean
and
y$chainMean
. If y$chainMean
does not exist this element equals
x$chainMean
.
Component chainVar
is the combination of x$chainVar
and y$chainVar
.
If y$chainVar
does not exist this element equals x$chainVar
.
Component pad
is a list containing various settings of the padded imputation
model, i.e. the imputation model after creating dummy variables. This list
is defined by combining x$pad
and y$pad
if y
is a
mids
object. Otherwise, it is defined by the settings of x
and
the combination of the data x$data
and y
.
Component loggedEvents
is set to x$loggedEvents
.
If a column of y
is categorical this is ignored in the
padded model since that column is not used as predictor for another column.
Karin Groothuis-Oudshoorn, Stef van Buuren, 2009
# append 'forgotten' variable bmi to imp temp <- boys[,c(1:3,5:9)] imp <- mice(temp,maxit=1,m=2) imp2 <- cbind.mids(imp, data.frame(bmi=boys$bmi)) # append maturation score to imp (numerical) mat <- (as.integer(temp$gen) + as.integer(temp$phb) + as.integer(cut(temp$tv,breaks=c(0,3,6,10,15,20,25)))) imp2 <- cbind.mids(imp, as.data.frame(mat)) # append maturation score to imp (factor) # known issue: new column name is 'y', not 'mat' mat <- as.factor(mat) imp2 <- cbind.mids(imp, mat) # append data frame with two columns to imp temp2 <- data.frame(bmi=boys$bmi,mat=as.factor(mat)) imp2 <- cbind.mids(imp, temp2) # combine two mids objects impa <- mice(temp, maxit=1, m=2) impb <- mice(temp2, maxit=2, m=2) # first a then b impab <- cbind.mids(impa, impb) # first b then a impba <- cbind.mids(impb, impa)