Multivariate Imputation by Chained Equations
Produces an object of class "mids", which stands for 'multiply imputed data set'.
mice(data, m = 5, imputationMethod = vector("character",length=ncol(data)), predictorMatrix = (1 - diag(1, ncol(data))), visitSequence = (1:ncol(data))[apply(is.na(data),2,any)], defaultImputationMethod=c("pmm","logreg","polyreg"), maxit = 5, diagnostics = TRUE, printFlag = TRUE, seed = NA)
- A data frame or a matrix containing the incomplete data. Missing values are coded as NA's.
- Number of multiple imputations. If omitted, m=5 is used.
- Can be either a 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 given method will be used for all columns. The default imputat
- 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 value
- 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 iteration of the algorithm. A column may be visited more than once. A
- A vector of three strings containing the default imputation methods for numerical columns, factor columns with 2 levels, and factor columns with more than two levels, respectively. If nothing is specified, the following defaults will be used:
- A scalar giving the number of iterations. The default is 5.
- 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
- An integer between 0 and 1000 that is used by the set.seed function for offsetting the random number generator. Default is to leave the random number generator alone.
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
oappropriate imputation 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 predictor set 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. Users may also write their own imputation functions, and call
these from within the algorithm.
In some cases, an imputation model may need transformed data in addition
to the original data (e.g. log or quadratic transforms). In order to
maintain consistency among different transformations of the same data,
the function has a special built-in method using the
~ mechanism. This
method can be used to ensure that a data transform always depends on the
most recently generated imputations in the untransformed (active)
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.
Built-in imputation methods are:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
For example, for the j'th column, the
impute.norm function that implements the
Bayesian linear regression method can be called by specifying the string "norm"
as the j'th entry in the vector of strings.
The user can write his or her own imputation function, say
impute.myfunc, and call it for all columns by specifying
imputationMethod="myfunc", or for specific columns by specifying
Some elementary imputation method require access to the nnet or MASS
libraries of Venables & Ripley. Where needed, these libraries will be
- An object of class mids, which stands for 'multiply imputed data set'. For
a description of the object, see the documentation on
Van Buuren, S. and Oudshoorn, C.G.M.. (1999). Flexible multivariate imputation by MICE. Report PG/VGZ/99.054, TNO Prevention and Health, Leiden. Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden. Van Buuren, S., Boshuizen, H.C. and 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, TNO Prevention and Health, Leiden and Erasmus University, Rotterdam.
data(nhanes) imp <- mice(nhanes) # do default multiple imputation on a numeric matrix imp imp$imputations$bmi # and list the actual imputations complete(imp) # show the first completed data matrix lm.mids(chl~age+bmi+hyp, imp) # repeated linear regression on imputed data data(nhanes2) mice(nhanes2,im=c("sample","pmm","logreg","norm")) # imputation on mixed data with a different method per column