R: Multivariate Imputation by Chained Equations

ds.mice {dsBaseClient}

R Documentation

Multivariate Imputation by Chained Equations

Description

This function calls the miceDS that is a wrapper function of the mice from the mice R package. The function creates multiple imputations (replacement values) for multivariate missing data. The method is based on Fully Conditional Specification, where each incomplete variable is imputed by a separate model. The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation. It is recommended that the imputation is done in each datasource separately. Otherwise the user should make sure that the input data have the same columns in all datasources and in the same order.

Usage

ds.mice(
  data = NULL,
  m = 5,
  maxit = 5,
  method = NULL,
  predictorMatrix = NULL,
  post = NULL,
  seed = NA,
  newobj_mids = NULL,
  newobj_df = NULL,
  datasources = NULL
)

Arguments

`data`	a data frame or a matrix containing the incomplete data.
`m`	Number of multiple imputations. The default is m=5.
`maxit`	A scalar giving the number of iterations. The default is 5.
`method`	Can be either a single string, or a vector of strings with length ncol(data), 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 in native R mice function. Columns that need not be imputed have the empty method "".
`predictorMatrix`	A numeric matrix of ncol(data) 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 to be imputed. A value of 1 means that the column variable is used as a predictor for the target variables (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.
`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. Multivariate (block) imputation methods ignore the post parameter.
`seed`	either NA (default) or "fixed". If seed is set to "fixed" then a fixed seed random number generator which is study-specific is used.
`newobj_mids`	a character string that provides the name for the output mids object that is stored on the data servers. Default `mids_object`.
`newobj_df`	a character string that provides the name for the output dataframes that are stored on the data servers. Default `imputationSet`. For example, if m=5, and newobj_df="imputationSet", then five imputed dataframes are saved on the servers with names imputationSet.1, imputationSet.2, imputationSet.3, imputationSet.4, imputationSet.5.
`datasources`	a list of `DSConnection-class` objects obtained after login. If the `datasources` argument is not specified the default set of connections will be used: see `datashield.connections_default`.

Details

For additional details see the help header of mice function in native R mice package.

Value

a list with three elements: the method, the predictorMatrix and the post.

Author(s)

Demetris Avraam for DataSHIELD Development Team

[Package dsBaseClient version 6.3.0 ]