DDC {cellWise}  R Documentation 
This function aims to detect cellwise outliers in the data. These are entries in the data matrix which are substantially higher or lower than what could be expected based on the other cells in its column as well as the other cells in its row, taking the relations between the columns into account. Note that this function first calls checkDataSet
and analyzes the remaining cleaned data.
DDC(X, DDCpars = list())
X 

DDCpars 
A list of available options:

A list with components:
DDCpars
The list of options used.
colInAnalysis
The column indices of the columns used in the analysis.
rowInAnalysis
The row indices of the rows used in the analysis.
namesNotNumeric
The names of the variables which are not numeric.
namesCaseNumber
The name of the variable(s) which contained the case numbers and was therefore removed.
namesNAcol
Names of the columns left out due to too many NA
's.
namesNArow
Names of the rows left out due to too many NA
's.
namesDiscrete
Names of the discrete variables.
namesZeroScale
Names of the variables with zero scale.
remX
Cleaned data after checkDataSet
.
locX
Estimated location of X
.
scaleX
Estimated scales of X
.
Z
Standardized remX
.
nbngbrs
Number of neighbors used in estimation.
ngbrs
Indicates neighbors of each column, i.e. the columns most correlated with it.
robcors
Robust correlations.
robslopes
Robust slopes.
deshrinkage
The deshrinkage factor used for every connected (i.e. nonstandalone) column of X
.
Xest
Predicted X
.
scalestres
Scale estimate of the residuals X  Xest
.
stdResid
Residuals of orginal X
minus the estimated Xest
, standardized by column.
indcells
Indices of the cells which were flagged in the analysis.
Ti
Outlyingness (test) value of each row.
medTi
Median of the Ti values.
madTi
Mad of the Ti values.
indrows
Indices of the rows which were flagged in the analysis.
indNAs
Indices of all NA cells.
indall
Indices of all cells which were flagged in the analysis plus all cells in flagged rows plus the indices of the NA cells.
Ximp
Imputed X
.
Raymaekers J., Rousseeuw P.J., Van den Bossche W.
Rousseeuw, P.J., Van den Bossche W. (2018). Detecting Deviating Data Cells. Technometrics, 60(2), 135145. (link to open access pdf)
Raymaekers, J., Rousseeuw P.J. (2019). Fast robust correlation for high dimensional data. Technometrics, published online. (link to open access pdf)
library(MASS); set.seed(12345) n < 50; d < 20 A < matrix(0.9, d, d); diag(A) = 1 x < mvrnorm(n, rep(0,d), A) x[sample(1:(n * d), 50, FALSE)] < NA x[sample(1:(n * d), 50, FALSE)] < 10 x[sample(1:(n * d), 50, FALSE)] < 10 x < cbind(1:n, x) DDCx < DDC(x) cellMap(DDCx$remX, DDCx$stdResid, columnlabels = 1:d, rowlabels = 1:n) # For more examples, we refer to the vignette: vignette("DDC_examples")