miceDS: Multivariate Imputation by Chained Equations (MICE)

In paularaissa/dsMiceClient: Distributed Statistical Computing - Server Side Package

Description

Generates Multivariate Imputations by Chained Equations (MICE)

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.

Usage

miceDS(
  vars = NULL,
  m = 5,
  method = NULL,
  predictorMatrix = NULL,
  where = NULL,
  blocks = NULL,
  visitSequence = NULL,
  formulas = NULL,
  blots = NULL,
  post = NULL,
  defaultMethod = c("pmm", "logreg", "polyreg", "polr"),
  maxit = 5,
  printFlag = TRUE,
  seed = NA,
  data.init = NULL
)

Arguments

m	Number of multiple imputations. The default is m=5.
method	Can be either a single string, or a vector of strings with length length(blocks), specifying the imputation method to be used for each column in data. If specified as a single string, the same method will be used for all blocks. The default imputation method (when no argument is specified) depends on the measurement level of the target column, as regulated by the defaultMethod argument. Columns that need not be imputed have the empty method. See details.
predictorMatrix	A numeric matrix of length(blocks) rows and ncol(data) columns, containing 0/1 data specifying the set of predictors to be used for each target column. Each row corresponds to a variable block, i.e., a set of variables to be imputed. A value of 1 means that the column variable is used as a predictor for the target block (in the rows). By default, the predictorMatrix is a square matrix of ncol(data) rows and columns with all 1's, except for the diagonal. Note: For two-level imputation models (which have "2l" in their names) other codes (e.g, 2 or -2) are also allowed.
where	A data frame or matrix with logicals of the same dimensions as data indicating where in the data the imputations should be created. The default specifies that the missing data should be imputed. The where argument may be used to overimpute observed data, or to skip imputations for selected missing values.
blocks	List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are imputed by a multivariate imputation method (see method argument). By default each variable is placed into its own block, which is effectively fully conditional specification (FCS) by univariate models (variable-by-variable imputation). Only variables whose names appear in blocks are imputed. The relevant columns in the where matrix are set to FALSE of variables that are not block members. A variable may appear in multiple blocks. In that case, it is effectively re-imputed each time that it is visited.
visitSequence	A vector of block names of arbitrary length, specifying the sequence of blocks that are imputed during one iteration of the Gibbs sampler. A block is a collection of variables. All variables that are members of the same block are imputed when the block is visited. A variable that is a member of multiple blocks is re-imputed within the same iteration. The default visitSequence = "roman" visits the blocks (left to right) in the order in which they appear in blocks. One may also use one of the following keywords: "arabic" (right to left), "monotone" (ordered low to high proportion of missing data) and "revmonotone" (reverse of monotone).
formulas	A named list of formula's, or expressions that can be converted into formula's by as.formula. List elements correspond to blocks. The block to which the list element applies is identified by its name, so list names must correspond to block names. The formulas argument is an alternative to the predictorMatrix argument that allows for more flexibility in specifying imputation models, e.g., for specifying interaction terms.
blots	A named list of alist's that can be used to pass down arguments to lower level imputation function. The entries of element blots are passed down to the function called for block blockname.
post	A vector of strings with length ncol(data) specifying expressions as strings. Each string is parsed and executed within the sampler() function to post-process imputed values during the iterations. The default is a vector of empty strings, indicating no post-processing.
defaultMethod	A vector of length 4 containing the default imputation methods for 1) numeric data, 2) factor data with 2 levels, 3) factor data with > 2 unordered levels, and 4) factor data with > 2 ordered levels. By default, the method uses pmm, predictive mean matching (numeric data) logreg, logistic regression imputation (binary data, factor with 2 levels) polyreg, polytomous regression imputation for unordered categorical data (factor > 2 levels) polr, proportional odds model for (ordered, > 2 levels).
maxit	A scalar giving the number of iterations. The default is 5.
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.
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 random draw from the data. Note that specification of data.init will start all m Gibbs sampling streams from the same imputation.
data	A data frame or a matrix containing the incomplete data. Missing values are coded as NA.
...	Named arguments that are passed down to the univariate imputation functions.

Value

Returns an S3 object of class mids

paularaissa/dsMiceClient documentation built on Dec. 22, 2021, 6:43 a.m.