cellHandler {cellWise}  R Documentation 
This function flags cellwise outliers in X
and imputes them, if robust estimates of the center mu
and scatter matrix Sigma
are given. When the latter are not known, as is typically the case, one can use the function DDC
which only requires the data matrix X
. Alternatively, the unknown center mu and scatter matrix Sigma can be estimated robustly from X
by the function DI
.
cellHandler(X, mu, Sigma, quant = 0.99)
X 

mu 
An estimate of the center of the data 
Sigma 
An estimate of the covariance matrix of the data 
quant 
Cutoff used in the detection of cellwise outliers. Defaults to 
A list with components:
Ximp
The imputed data matrix.
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,...)
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 < rbind(c(0.5, 1.0, 5.0), c(3.0, 0.0, 1.0)) n < nrow(X); d < ncol(X) out < cellHandler(X, mu, Sigma) Xres < X  out$Ximp # unstandardized residual mean(abs(as.vector(Xres  out$Zres*out$Zres_denom))) # 0 W < matrix(rep(0,n*d),nrow=n) # weight matrix W[out$Zres != 0] < 1 # 1 indicates cells that were flagged # For more examples, we refer to the vignette: vignette("DI_examples")