Imputation by Bayesian linear regression — impute.mice

Apply imputation to the data by Bayesian linear regression (Rubin 1987; Schafer 1997; van Buuren and Groothuis-Oudshoorn 2011) .

Usage

impute.mice_norm(dataSet, m = 5, seed = 362436069)

Arguments

dataSet: A data frame containing the data signals.
m: An integer (default = 5) specifying the number of multiple imputations.
seed: An integer (default = 362436069) specifying the seed used for the random number generator for reproducibility.

Value

The imputed data.

References

Rubin DB (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley \& Sons, New York, NY, USA. ISBN 9780471087052.

Schafer JL (1997). Analysis of Incomplete Multivariate Data. Chapman \& Hall/CRC, New York, NY, USA. ISBN 9780412040610.

van Buuren S, Groothuis-Oudshoorn K (2011). “mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software, 45(3), 1–67. doi:10.18637/jss.v045.i03 .