cbind.mids {mice}R Documentation

Combine a Multiply Imputed Data Set with other mids object or dataframe

Description

Columnwise combination of mids objects

Usage

cbind.mids(x,y,...)

Arguments

x

A mids object.

y

A mids object or a dataframe, matrix, factor or vector.

...

Dataframes, matrices, vectors or factors. These can be given as named arguments.

Details

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 dataframe 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.

Value

call

A vector, with first argument the mice() statement that created x and second argument the call to cbind.mids().

data

The cbind of the (incomplete) data in x$data and y$data.

m

The number of imputations.

nmis

An array containing the number of missing observations per column.

imp

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.

method

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 "".

predictorMatrix

A square matrix of size ncol(data) containing code 0/1 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.

visitSequence

The sequence in which columns are visited. The same as x$visitSequence.

seed

The seed value of the solution, x$seed.

iteration

Last Gibbs sampling iteration number, x$iteration.

lastSeedValue

The most recent seed value, x$lastSeedValue

chainMean

Combination of x$chainMean and y$chainMean. If y$chainMean does not exist this element equals x$chainMean.

chainVar

Combination of x$chainVar and y$chainVar. If y$chainVar does not exist this element equals x$chainVar.

pad

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.

Remark that 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.

Author(s)

Karin Groothuis-Oudshoorn, Stef van Buuren, 2009

See Also

rbind.mids, ibind, mids

Examples

# append 'forgotten' variable bmi to imp
temp <- boys[,c(1:3,5:9)]
imp  <- mice(temp,maxit=1)
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=2)
impb <- mice(temp2, maxit=3)

# first a then b
impab <- cbind.mids(impa, impb)

# first b then a
impba <- cbind.mids(impb, impa)

[Package mice version 2.11 Index]