DI {cellWise}  R Documentation 
The DetectionImputation 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 reestimation of the center and covariance matrix. By default, it starts by calling checkDataSet
to clean the data.
DI(X, initEst = "DDCWcov", crit = 0.01, maxits = 10, quant = 0.99, maxCol = 0.25, checkPars = list())
X 

initEst 
An initial estimator for the center and covariance matrix. Should be one of 
crit 
The algorithm converges when the subsequent estimates of the center and covariance matrix do not differ more than 
maxits 
Maximum number of DIiterations. 
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

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 DIiterations 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.
J. Raymaekers and P.J. Rousseeuw
J. Raymaekers and P.J. Rousseeuw (2020). Handling cellwise outliers by sparse regression and robust covariance. Arxiv: 1912.12446. (link to open access pdf)
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: vignette("DI_examples")