# DI examples

## Raymaekers, J. and Rousseeuw, P.J.

### 2026-02-25

# Introduction

This file contains some examples of the functions related to the DI and cellHandler routines.

    library("cellWise")

# Artificial data

In this example we consider an artificial dataset with cellwise outliers. First we construct a correlation matrix and then use it to generate the data.

    d     <- 10
    mu    <- rep(0, 10)
    Sigma <- generateCorMat(d = d, corrType = "A09")

    n      <- 100 # number of observations
    outlierType   <- "cellwiseStructured" # type of cellwise outliers
    perout <- 0.2 # percentage of outliers
    gamma  <- 5 # how far the outliers are from the center

    data  <- generateData(n, d, mu, Sigma, perout,
                          gamma, outlierType, seed = 1)
    X <- data$X
    pairs(X)

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    # we clearly see some marginal outliers, but also some more tricky ones.

Now we run the DI algorithm to detect cellwise outliers

    tic = Sys.time()
    DI.out = DI(X)

    ##
    ##  The input data has 100 rows and 10 columns.

    toc = Sys.time(); toc - tic

    ## Time difference of 0.177187 secs

    DI.out$nits

    ## [1] 5

    # the algorithm converges in 5 iterations and takes under 1 second

    flaggedCells <- DI.out$indcells # indices of the flagged Cells
    length(intersect(data$indcells, flaggedCells))

    ## [1] 155

    # 155 of the 200 cells are flagged

    # We can now compare this with the marginal flagging of outliers.
    locScale.out <- estLocScale(X) # robust location and scale of X
    Z <- scale(X, locScale.out$loc, locScale.out$scale)
    flaggedCells_marginal <- which(abs(Z) >  sqrt(qchisq(p = 0.99, df = 1)))
    length(intersect(data$indcells, flaggedCells_marginal))

    ## [1] 57

    # only 61 of the 200 cells are flagged

    cellMap(D = X, R = DI.out$Zres, indcells = flaggedCells,
            mTitle = "cellHandler")

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    cellMap(D = X, R = Z, indcells = flaggedCells_marginal,
            mTitle = "marginal analysis")

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# VOC data

We first load an inspect the data.

    data("data_VOC")
    # ?VOC

    # The first 16 variables are the VOCs, the last 3 are:
    # SMD460: How many people who live here smoke tobacco?
    # SMD470: How many people smoke inside this home?
    # RIDAGEYR: age

    colnames(data_VOC)

    ##  [1] "URX2MH"   "URX34M"   "URXAAM"   "URXAMC"   "URXATC"   "URXBMA"
    ##  [7] "URXCEM"   "URXCYM"   "URXDHB"   "URXHP2"   "URXHPM"   "URXIPM3"
    ## [13] "URXMAD"   "URXMB3"   "URXPHG"   "URXPMM"   "SMD460"   "SMD470"
    ## [19] "RIDAGEYR"

    range(data_VOC$RIDAGEYR)

    ## [1]  3 10

    # the subjects in this dataset are children between 3 and 10

Now extract the VOC data and run the DI algorithm to estimate a covariance matrix and flag cellwise outliers.

    X <- data_VOC[, -c(17:19)] # extract the VOC data
    dim(X)

    ## [1] 512  16

    rownames(X) = 1:512
    # Run the Detection Imputation (DI) algorithm:
    tic = Sys.time()
    DI.out = DI(X)

    ##
    ##  The input data has 512 rows and 16 columns.

    toc = Sys.time(); toc - tic

    ## Time difference of 1.614245 secs

    DI.out$nits

    ## [1] 4

    # the algorithm converges in 4 iterations and takes roughly 2 seconds

    Zres     <- DI.out$Zres
    dimnames(Zres) <- dimnames(X)
    indcells <- DI.out$indcells
    # Draw cellmap:
    # pdf("VOCs_20_cellmap.pdf", height = 5, width = 5)
    rowsToShow = 1:20
    cellMap(Zres, showrows = rowsToShow,
            mTitle = "VOCs in children",
            rowtitle = "first 20 children",
            columntitle = "volatile components")

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    # dev.off()
    rm(rowsToShow)

Now analyze the flagged cells and compare them with the cells found based on marginal outlyingness.

    W <- matrix(0, nrow(X), ncol(X))
    W[indcells] <- 1
    # Variable 8 has a substantial number of red cells.
    # Its total number of outlying cells:
    sum(W[,8])/nrow(X)

    ## [1] 0.109375

    # Variable 8 has 11% of outlying cells.

    # Since quant = 0.99 these are the cells with absolute
    # residual above sqrt(qchisq(p=0.99,df=1)) = 2.575829 .
    # Variable 8 is "URXCYM":
    #   N-Acetyl-S-(2-cyanoethyl)-L-cysteine (ng/mL)
    # this is a well-known biomarker for exposure to tobacco
    # smoke. see e.g.
    # https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210104
    # Adults who smoke usually have high values.

    # How many URXCYM values in this set are marginally outlying?
    # If we would use univariate outlier detection, few of
    # the URXCYM values in this set would be considered suspicious:
    meds = apply(X,2,FUN="median")
    mads = apply(X,2,FUN="mad")
    Z = scale(X,center=meds,scale=mads)
    cutoff = sqrt(qchisq(p=0.99,df=1)); cutoff

    ## [1] 2.575829

    cellInd = which(abs(Zres[,8]) > cutoff)
    length(cellInd)/nrow(X) # almost 11%

    ## [1] 0.109375

    marginalInd = which(abs(Z[,8]) > cutoff)
    length(marginalInd)/nrow(X) # under 2%

    ## [1] 0.01953125

    # Even for perfectly gaussian data this would already be 1%.

    # pdf("ZresVersusZ.pdf",width=5.4,height=5.4) # sizes in inches
    plot(Z[,8], Zres[,8], xlab = "",
         ylab = "",main = "",pch = 16,
         col = "black", xlim=c(-3,5))
    title(main="log(URXCYM) in children aged 10 or younger",
          line=1) # , cex.lab=1.2, family="Calibri Light")
    title(ylab="standardized cellwise residuals", line=2.3)
    title(xlab="robustly standardized marginal values", line=2.3)
    abline(h=cutoff, col="red")
    abline(h=-cutoff, col="red")
    abline(v=cutoff, col="red")
    abline(v=-cutoff, col="red")

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    # dev.off()
    # Many outlying residuals occur at inlying marginal values.
    # These persons' URXCYM is high relative to the other
    # compounds (variables) in the same person.

We now link the findings with the data on household smoking.

    # Look at the residuals for the children who
    # live together with people who smoke.
    # We consider 4 categories:

    # children without smokers in their family:
    nonsmokers = which(data_VOC$SMD460 == 0)
    length(nonsmokers)

    ## [1] 340

    # at least one adult smokes, but not in the home:
    noneInHome = which((data_VOC$SMD460 > 0) &
                         (data_VOC$SMD470 == 0))
    length(noneInHome)

    ## [1] 109

    # children with 1 person smoking in their home:
    oneInHome = which(data_VOC$SMD470 == 1)
    length(oneInHome)

    ## [1] 33

    # children with 2 people smoking in their home:
    twoInHome = which(data_VOC$SMD470 == 2)
    length(twoInHome)

    ## [1] 11

    length(which(Zres[nonsmokers,8] > 0))/length(nonsmokers)

    ## [1] 0.05294118

    length(which(Zres[noneInHome,8] > 0))/length(noneInHome)

    ## [1] 0.1100917

    length(which(Zres[oneInHome,8] > 0))/length(oneInHome)

    ## [1] 0.3636364

    length(which(Zres[twoInHome,8] > 0))/length(twoInHome)

    ## [1] 0.6363636

    # So 36% of the children living in a house with one smoker
    # have suspiciously high levels for this biomarker.
    # 63% of the children living in a house with two smokers
    # have suspiciously high levels for this biomarker.

    cellMap(Zres,
            showrows = oneInHome,
            mTitle = "VOCs in children",
            rowtitle = "",
            columntitle = "volatile components")

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    cellMap(Zres,
            showrows = twoInHome,
            mTitle = "VOCs in children",
            rowtitle = "",
            columntitle = "volatile components")

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    # For one or more smokers in the house:
    smokeInHome = c(oneInHome,twoInHome)
    length(smokeInHome)

    ## [1] 44

    cellMap(Zres,
            showrows = smokeInHome,
            mTitle = "VOCs in children",
            rowtitle = "",
            columntitle = "volatile components")

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In all of these cellmaps the variable URXCYM stands out!

If we would use a univariate detection bound, many of these values wouldn’t be considered suspicious:

    length(which(Z[nonsmokers] > cutoff))/length(nonsmokers)

    ## [1] 0.02058824

    length(which(Z[noneInHome] > cutoff))/length(noneInHome)

    ## [1] 0.02752294

    length(which(Z[oneInHome] > cutoff))/length(oneInHome)

    ## [1] 0

    length(which(Z[twoInHome] > cutoff))/length(twoInHome)

    ## [1] 0

    # Here the fractions are not even increasing with the number of smokers.

    plotdata = matrix(c(length(which(Zres[nonsmokers,8] > 0))/length(nonsmokers),
                        length(which(Zres[noneInHome,8] > 0))/length(noneInHome),
                        length(which(Zres[oneInHome,8] > 0))/length(oneInHome),
                        length(which(Zres[twoInHome,8] > 0))/length(twoInHome),
                        length(which(Z[nonsmokers] > cutoff))/length(nonsmokers),
                        length(which(Z[noneInHome] > cutoff))/length(noneInHome),
                        length(which(Z[oneInHome] > cutoff))/length(oneInHome),
                        length(which(Z[twoInHome] > cutoff))/length(twoInHome)),
                      nrow = 2, byrow = TRUE)

    # pdf("cellwise_marginal.pdf",width=5.4,height=5.4)
    matplot(1:4, t(plotdata), type = "b", pch = 16, lwd = 3,
            cex = 2, xlab = "", xaxt = "n", ylab = "", yaxt = "n",
            ylim = c(0, 0.7), col = c("blue", "red"), lty = 1)
    axis(side = 1, labels = c("none", "0 in home", "1 in home", "2 in home"),
         at = 1:4, cex.axis = 1.3)
    axis(side = 2, labels = seq(0, 100, by = 20),
         at = seq(0, 1, by = 0.2), cex.axis = 1.3)
    legend("topleft", fill = c("blue", "red"),
           legend = c("cell residuals","marginal values"), cex = 1.3)
    title(main="Effect of smokers on elevated URXCYM in children",
          line=1.2)
    title(ylab="% of children with elevated URXCYM",cex.lab=1.3, line=2.3)
    title(xlab="smoking adults in household",cex.lab=1.3, line=2.3)

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    # dev.off()