cellWise (version 2.5.3)

DI: Detection-Imputation algorithm

Description

The Detection-Imputation algorithm computes cellwise robust estimates of the center and covariance matrix of a data set X. The algorithm alternates between the detection of cellwise outliers and their imputation combined with re-estimation of the center and covariance matrix. By default, it starts by calling checkDataSet to clean the data.

Usage

DI(X, initEst = "DDCWcov", crit = 0.01, maxits = 10, quant = 0.99,
maxCol = 0.25, checkPars = list())

Value

A list with components:

• center
  The final estimate of the center of the data.

• cov
  The final estimate of the covariance matrix.

• nits
  Number of DI-iterations executed to reach convergence.

• Ximp
  The imputed data.

• indcells
  Indices of the cells which were flagged in the analysis.

• indNAs
  Indices of the NAs in the data.

• Zres
  Matrix with standardized cellwise residuals of the flagged cells. Contains zeroes in the unflagged cells.

• Zres_denom
  Denominator of the standardized cellwise residuals.

• cellPaths
  Matrix with the same dimensions as X, in which each row contains the path of least angle regression through the cells of that row, i.e. the order of the coordinates in the path (1=first, 2=second,...)

• checkDataSet_out
  Output of the call to checkDataSet which is used to clean the data.

Arguments

X

X is the input data, and must be an \(n\) by \(d\) matrix or a data frame.

initEst

An initial estimator for the center and covariance matrix. Should be one of "DDCWcov" or "TSGS", where the latter refers to the function GSE::TSGS. The default option "DDCWcov" uses the proposal of Raymaekers and Rousseeuw (2020) which is much faster for increasing dimension.

crit

The algorithm converges when the subsequent estimates of the center and covariance matrix do not differ more than crit in squared Euclidean norm.

maxits

Maximum number of DI-iterations.

quant

The cutoff used to detect cellwise outliers.

maxCol

The maximum number of cellwise outliers allowed in a column.

checkPars

Optional list of parameters used in the call to checkDataSet. The options are:

• coreOnly
  If TRUE, skip the execution of checkDataset. Defaults to FALSE

• numDiscrete
  A column that takes on numDiscrete or fewer values will be considered discrete and not retained in the cleaned data. Defaults to \(5\).

• fracNA Only retain columns and rows with fewer NAs than this fraction. Defaults to \(0.15\).

• precScale
  Only consider columns whose scale is larger than precScale. Here scale is measured by the median absolute deviation. Defaults to \(1e-12\).

• silent
  Whether or not the function progress messages should be suppressed. Defaults to FALSE.

Author

J. Raymaekers and P.J. Rousseeuw

References

J. Raymaekers and P.J. Rousseeuw (2020). Handling cellwise outliers by sparse regression and robust covariance. Journal of Data Science, Statistics, and Visualisation. tools:::Rd_expr_doi("10.52933/jdssv.v1i3.18")(link to open access pdf)

See Also

cellHandler

Examples

mu <- rep(0, 3)
Sigma <- diag(3) * 0.1 + 0.9
X <- MASS::mvrnorm(100, mu, Sigma)
DI.out <- DI(X)
DI.out$cov
# For more examples, we refer to the vignette:
if (FALSE) {
vignette("DI_examples")
}