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

```
library("cellWise")
```

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)
```

```
# 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 0.1564331 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")
```

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

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 1.172813 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")
```

```
# 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] 0.1074219
```

```
# 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] 0.1074219
```

```
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")
```

```
# 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] 0.05
```

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

```
## [1] 0.1009174
```

```
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")
```

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

```
# 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")
```

In all of these cellmaps the variable URXCYM stands out!

If we would use a univariate detection bound, many of these values wouldnâ€™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)
```

```
# dev.off()
```