mice (version 2.18)

mice: Multivariate Imputation by Chained Equations (MICE)

Description

Generates Multivariate Imputations by Chained Equations (MICE)

Usage

mice(data, m = 5,
    method = vector("character", length = ncol(data)),
    predictorMatrix = (1 - diag(1, ncol(data))),
    visitSequence = (1:ncol(data))[apply(is.na(data), 2, any)],
    form = vector("character", length = ncol(data)),
    post = vector("character", length = ncol(data)),
    defaultMethod = c("pmm", "logreg", "polyreg", "polr"),
    maxit = 5, diagnostics = TRUE, printFlag = TRUE,
    seed = NA, imputationMethod = NULL,
    defaultImputationMethod = NULL, data.init = NULL, ...)

Arguments

data

A data frame or a matrix containing the incomplete data. Missing values are coded as NA.

m

Number of multiple imputations. The default is m=5.

method

Can be either a single string, or a vector of strings with length ncol(data), specifying the elementary imputation method to be used for each column in data. If specified as a single string, the same method will be used for all column

predictorMatrix

A square matrix of size ncol(data) containing 0/1 data specifying the set of predictors to be used for each target column. Rows correspond to target variables (i.e. variables to be imputed), in the sequence as they appear in data. A v

visitSequence

A vector of integers of arbitrary length, specifying the column indices of the visiting sequence. The visiting sequence is the column order that is used to impute the data during one pass through the data. A column may be visited more than once. A

post

A vector of strings with length ncol(data), specifying expressions. Each string is parsed and executed within the sampler() function to postprocess imputed values. The default is to do nothing, indicated by a vector of e

form

A vector of strings with length ncol(data), specifying formulae. Each string is parsed and executed within the sampler() function to create terms for the predictor. The default is to do nothing, indicated by a vector of

defaultMethod

A vector of three strings containing the default imputation methods for numerical columns, factor columns with 2 levels, and columns with (unordered or ordered) factors with more than two levels, respectively. If nothing is specified, the followin

maxit

A scalar giving the number of iterations. The default is 5.

diagnostics

A Boolean flag. If TRUE, diagnostic information will be appended to the value of the function. If FALSE, only the imputed data are saved. The default is TRUE.

printFlag

If TRUE, mice will print history on console. Use print=FALSE for silent computation.

seed

An integer that is used as argument by the set.seed() for offsetting the random number generator. Default is to leave the random number generator alone.

imputationMethod

Same as method argument. Included for backwards compatibility.

defaultImputationMethod

Same as defaultMethod argument. Included for backwards compatibility.

data.init

A data frame of the same size and type as data, without missing data, used to initialize imputations before the start of the iterative process. The default NULL implies that starting imputation are created by a simple ran

...

Named arguments that are passed down to the elementary imputation functions.

Value

• Returns an S3 object of class mids (multiply imputed data set)

emph

Passive imputation:

code

~

Details

Generates multiple imputations for incomplete multivariate data by Gibbs sampling. Missing data can occur anywhere in the data. The algorithm imputes an incomplete column (the target column) by generating 'plausible' synthetic values given other columns in the data. Each incomplete column must act as a target column, and has its own specific set of predictors. The default set of predictors for a given target consists of all other columns in the data. For predictors that are incomplete themselves, the most recently generated imputations are used to complete the predictors prior to imputation of the target column.

A separate univariate imputation model can be specified for each column. The default imputation method depends on the measurement level of the target column. In addition to these, several other methods are provided. You can also write their own imputation functions, and call these from within the algorithm.

The data may contain categorical variables that are used in a regressions on other variables. The algorithm creates dummy variables for the categories of these variables, and imputes these from the corresponding categorical variable. The extended model containing the dummy variables is called the padded model. Its structure is stored in the list component pad.

Built-in elementary imputation methods are:

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

These corresponding functions are coded in the mice library under names mice.impute.method, where method is a string with the name of the elementary imputation method name, for example norm. The method argument specifies the methods to be used. For the j'th column, mice() calls the first occurence of paste('mice.impute.',method[j],sep='') in the search path. The mechanism allows uses to write customized imputation function, mice.impute.myfunc. To call it for all columns specify method='myfunc'. To call it only for, say, column 2 specify method=c('norm','myfunc','logreg',...)

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/

van Buuren, S. (2012). Flexible Imputation of Missing Data. Boca Raton, FL: Chapman & Hall/CRC Press.

Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76, 12, 1049--1064.

Van Buuren, S. (2007) Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research, 16, 3, 219--242.

Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine, 18, 681--694.

Brand, J.P.L. (1999) Development, implementation and evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets. Dissertation. Rotterdam: Erasmus University.

See Also

mids, with.mids, set.seed, complete

Examples

# do default multiple imputation on a numeric matrix
imp <- mice(nhanes)
imp

# list the actual imputations for BMI
imp$imputations$bmi

# first completed data matrix
complete(imp)


# imputation on mixed data with a different method per column

mice(nhanes2, meth=c('sample','pmm','logreg','norm'))