mi.info: Function to create information matrix for missing data...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/mi.info.R

Description

Produces matrix of information needed to impute the missing data. After the information is extracted user has the option of changing the default.

Usage

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  mi.info(data, threshold  = 0.99999)
  ## S4 method for signature 'mi.info'
print(x, ...)
  ## S4 method for signature 'mi.info'
show(object)

Arguments

data

dataframe or matrix of dataset with missing data coded as NAs.

threshold

Threshold value for correlation to be considered a problem.

x

An object of a class mi.info.

object

An object of a class mi.info.

...

Currently not used.

Value

info

information matrix

-name: Name of variable
-imp.order: Imputation Order
-nmis: Number of missing
-type: Type of variable
-var.class: Class of input variable
-level: Levels in the input varialbe
-include: Include in the imputation process or not
-is.ID: Is ID variable or not
-all.missing: All observation missing or not
-collinear: Collineared variables
-determ.pred: Deterministic predictor
-imp.formula: Imputation formula
-params: Parameters for the imputation model
-other: Currently not used

Author(s)

Masanao Yajima [email protected], M.Grazia Pittau [email protected], Andrew Gelman [email protected]

References

Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).

See Also

mi

Examples

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  data(CHAIN)
  info.CHAIN <- mi.info(CHAIN)
  
  info.CHAIN$imp.order # imputation order
  
  info.CHAIN$imp.formula # imputation formula
  info.CHAIN[["age.W1"]]$imp.formula  #imputation formula for specific variable

mi documentation built on May 31, 2017, 1:51 a.m.

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