mi.info {mi} | R Documentation |
Function to create information matrix for missing data imputation
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
mi.info(data, threshhold = 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. |
threshhold |
Threshhold 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 yajima@stat.columbia.edu,
M.Grazia Pittau grazia@stat.columbia.edu,
Andrew Gelman gelman@stat.columbia.edu
References
Andrew Gelman and M. Grazia Pittau,
A flexible program for missing-data imputation and model checking,
Technical report, Columbia University, New York; Andrew Gelman and Jennifer Hill,
Data Analysis Using Regression and Multilevel/Hierarchical Models,
Cambridge University Press, 2007.
See Also
mi
Examples
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
[Package
mi version 0.08-03
Index]