Reuse a previously fit multivariate imputation by chained equations to impute values for previously unseen data without changing the imputation fit (i.e. solely use the original training data to guide the imputation models).
Note: see https://github.com/stefvanbuuren/mice/issues/32 for discussion
mice.reuse(mids, newdata, maxit = 5, printFlag = TRUE, seed = NA)data : list of data.frames the imputations of newdata
lastSeedValue : integer vector the random seed at the end of the procedure
: mids object An object of class mids, typically produces by a previous call to mice() or mice.mids()
: data.frame Previously unseen data of the same structur as used to generate `mids`
: integer scalar The number of additional Gibbs sampling iterations to refine the new imputations
: logical scalar A Boolean flag. If TRUE, diagnostic information during the Gibbs sampling iterations will be written to the command window. The default is TRUE.
: integer scalar An integer that is used as argument by the set.seed() for offsetting the random number generator. Default is to use the last seed value stored in `mids`
Patrick Rockenschaub git https://github.com/prockenschaub