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Imputation by Bayesian linear regression

Source: R/imputations.R
impute.mice_norm.Rd

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

Usage

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

Arguments

dataSet

The 2d dataset of experimental values.

reportImputing

A boolean (default = FALSE) specifying whether to provide a shadow data frame with imputed data labels, where 1 indicates the corresponding entries have been imputed, and 0 indicates otherwise. Alters the return structure.

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

  • If reportImputing = FALSE, the function returns the imputed 2d dataframe.

  • If reportImputing = TRUE, the function returns a list of the imputed 2d dataframe and a shadow matrix showing which proteins by replicate were imputed.

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 .