mi.preprocess: Preproessing and Postprocessing mi data object
In mi: Missing Data Imputation and Model Checking

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

Function for propressing and postprocessing nonnegative, and positive-continuous variable types in mi data object

1 2	mi.preprocess(data, info) mi.postprocess(mi.data, info)

`data`	the data.frame to be imputed.
`info`	the information matrix, see `mi.info`.
`mi.data`	the imputed data list, obtained from `mi.completed`

mi.proprocess will transform the nonnegative and positive-continuous variable types. If the variable is of nonnegative type, the function transforms the variable into two variables: an indicator indicates whether the value is postive or not and a transformed variable that takes on all positive value and is transformed either by taking a log; 0 and NA will be treated as missing for such a variable. If the variable is of positive-continuous type, it will be transformed by taking a log.

mi.postprocess will transform the imputed dataset back to its original form. The imputed dataset is obtained from mi.completed function.

`data`	a `data.frame` or a list of dataframe
`mi.info`	a `mi.info` matrix

Yu-Sung Su suyusung@tsinghua.edu.cn, Andrew Gelman gelman@stat.columbia.edu

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).

mi.completed

mi documentation built on May 2, 2019, 4:43 p.m.