impseqrob: Robust sequential imputation of missing values

In rrcovNA: Scalable Robust Estimators with High Breakdown Point for Incomplete Data

Robust sequential imputation of missing values

Description

Impute missing multivariate data using robust sequential algorithm

Usage

impSeqRob(x, alpha=0.9)

Arguments

x	the original incomplete data matrix.
alpha	.The default is alpha=0.9.

Details

SEQimpute starts from a complete subset of the data set Xc and estimates sequentially the missing values in an incomplete observation, say x*, by minimizing the determinant of the covariance of the augmented data matrix X* = [Xc; x']. Then the observation x* is added to the complete data matrix and the algorithm continues with the next observation with missing values. Since SEQimpute uses the sample mean and covariance matrix it will be vulnerable to the influence of outliers and it is improved by plugging in robust estimators of location and scatter. One possible solution is to use the outlyingness measure as proposed by Stahel (1981) and Donoho (1982) and successfully used for outlier identification in Hubert et al. (2005). We can compute the outlyingness measure for the complete observations only but once an incomplete observation is imputed (sequentially) we could compute the outlyingness measure for it too and use it to decide if this observation is an outlier or not. If the outlyingness measure does not exceed a predefined threshold the observation is included in the further steps of the algorithm.

Value

a matrix of the same form as x, but with all missing values filled in sequentially.

References

S. Verboven, K. Vanden Branden and P. Goos (2007). Sequential imputation for missing values. Computational Biology and Chemistry, 31, 320–327. K. Vanden Branden and S. Verboven (2009). Robust Data Imputation. Computational Biology and Chemistry, 33, 7–13.

Examples

    data(bush10)
    impSeqRob(bush10) # impute squentially missing data

rrcovNA documentation built on Sept. 11, 2024, 8:12 p.m.