---
title: "Vignette: Weighted BACON algorithms"
author: "Tobias Schoch"
output:
    html_document:
        highlight: tango
vignette: >
  %\VignetteIndexEntry{Weighted BACON algorithms}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "",
    prompt = TRUE
)
```

```{css, echo = FALSE}
.my-callout {
    padding: 1.25rem;
    margin-top: 1.25rem;
    margin-bottom: 1.25rem;
    border: 1px solid #eee;
    border-left-width: 0.75rem;
    border-left-color: #df536b;
    border-radius: .25rem
}
```

## Introduction

The package `wbacon` implements a weighted variant of the BACON (blocked adaptive computationally-efficient outlier nominators) algorithms [Billor et al.](#biblio) (2000) for multivariate outlier detection and robust linear regression. The extension of the BACON algorithm for outlier detection to allow for weighting is due to [Béguin and Hulliger](#biblio) (2008).

### Available methods

* `wBACON()` is for multivariate outlier nomination and robust estimation of location/ center and covariance matrix
* `wBACON_reg()` is for robust linear regression (the method is robust against outliers in the response variable and the model's design matrix)

### Assumptions

The BACON algorithms assume that the underlying model is an appropriate description of the non-outlying observations; [Billor et al.](#biblio) (2000). More precisely,

* the outlier nomination method assumes that the "good" data have (roughly) an *elliptically contoured* distribution (this includes the Gaussian distribution as a special case);
* the regression method assumes that the non-outlying ("good") data are described by a *linear* (homoscedastic) regression model and that the independent variables (having removed the regression intercept/constant, if there is a constant) follow (roughly) an elliptically contoured distribution.

<div class="my-callout">
<p><i>"Although the algorithms will often do something reasonable even when these assumptions are violated, it is hard to say what the results mean."</i> [Billor et al.](#biblio) (2000, p. 290)</p>
</div>

It is strongly recommended that the structure of the data be examined and whether the assumptions made about the "good" observations are reasonable.

### The role of the data analyst

In line with [Billor et al.](#biblio) (2000, p. 290), we use the term outlier "nomination" rather than "detection" to highlight that algorithms should not go beyond nominating observations as *potential* outliers; see also [Béguin and Hulliger](#biblio) (2008). It is left to the analyst to finally label outlying observations as such.

The software provides the analyst with tools and measures to study potentially outlying observations. It is strongly recommended to use the tools.

### Additional information

Additional information on the BACON algorithms and the implementation can be found in the documents:

* `methods.pdf`: A mathematical description of the algorithms and their implementation;
* `doc_c_functions.pdf`: A documentation of the `C` functions.

Both documents can be found in the package folder `doc`.

## Installation

Make sure that the package `devtools` is installed.<sup>[1](#notes)</sup> Then, the `wbacon` package can be pulled and installed from the [GitHub](https://github.com/tobiasschoch/wbacon) repository using


```{r, eval = FALSE}
devtools::install_github("tobiasschoch/wbacon")
```

The package contains `C` code that needs to be compiled.  Users of Microsoft Windows need an installation of the R tool chain bundle [rtools40](https://cran.r-project.org/bin/windows/Rtools/) to build the package.

Once the package has been installed, it can be loaded and attached to the current R session by

```{r}
library(wbacon)
```

## Multivariate outlier detection

In this section, we study multivariate outlier detection for the two datasets

* bushfire data (with sampling weights),
* philips data (without sampling weights).


### Bushfire data

The bushfire dataset is on satellite remote sensing. These data were used by [Campbell](#biblio) (1984) to locate bushfire scars. The data are radiometer readings from polar-orbiting satellites of the National Oceanic and Atmospheric Administration (NOAA) which have been collected continuously since 1981. The measurements are taken on five frequency bands or channels. In the near infrared band, it is possible to distinguish vegetation types from burned surface. At visible wavelengths, the vegetation spectra are similar to burned surface. The spatial resolution is rather low (1.1 km per pixel).

#### Data preparation

The bushfire data contain radiometer readings for 38 pixels and have been studied in [Maronna and Yohai](#biblio) (1995), [Béguin and Hulliger](#biblio) (2002), [Béguin and Hulliger](#biblio) (2008), and [Hulliger and Schoch](#biblio) (2009). The data can be obtained from the `R` package `modi`([Hulliger and Sterchi](#biblio), 2020).<sup>[2](#notes)</sup>

```{r}
data(bushfire, package = "modi")
```

The first 6 readings on the five frequency bands (variables) are

```{r}
head(bushfire)
```

[Béguin and Hulliger](#biblio) (2008) generated a set of sampling weights. The weights can be attached to the current session by

```{r}
data(bushfire.weights, package = "modi")
```

#### Outlier detection

```{r}
fit <- wBACON(bushfire, w = bushfire.weights, alpha = 0.05)
fit
```

The argument `alpha` determines the $(1-\alpha)$-quantile $\chi_{\alpha,d}^2$ of the chi-square distribution with $d$ degrees of freedom.<sup>[3](#notes)</sup> All observations whose squared Mahalanobis distances are smaller than the quantile (times a correction factor) are selected into the subset of outlier-free data. It is recommended to choose `alpha` on grounds of an educated guess of the share of "good" observations in the data. Here, we suppose that 95\% of the observations are not outliers.

By default, the initial subset is determined by the Euclidean norm (initialization method: `version = "V2"`).

* This initialization method is robust because it is based on the coordinate-wise (weighted) median. The resulting estimators of center and scatter are *not affine equivariant*. Let $T(\cdot)$ denote an estimator of a parameter of interest (e.g., covariance matrix) and let $X$ denote the $(n \times p)$ data matrix. An estimator $T$ is affine equivariant if and only if $T(A X + b) = A T(X) + b$, for any nonsingular $(m \times n)$ matrix $A$ and any $n$-vector $b$. Although version `"V2"` of the BACON method yields an estimator that is not affine equivariant in the above sense, [Billor et al.](#biblio) (2000) point out that the method is nearly affine equivariant.
* There exists an alternative initialization method (`"version = V1"`) which is based on the coordinate-wise (weighted) means; therefore, it is affine equivariant but *not robust*.

From the above output, we see that the algorithm converged in three iterations. In case the algorithm does not converge, we may increase the maximum number of iterations (default: `maxiter = 50`) and toggle `verbose = TRUE` to (hopefully) learn more why the method did not converge.

In the next step, we want to study the result in more detail. In particular, we are interested in the estimated center and scatter (or covariance) matrix. To this end, we can call the `summary()` method on the object `fit`.

```{r}
summary(fit)
```

#### Diagnostics

The method has detected `r sum(is_outlier(fit))` *potential* outliers. It is important to study the diagnostic plot to learn more about the potential outliers. The robust (Mahalanobis) distances vs. the index of the observations (`1:n`) can be plotted as follows.

```{r}
plot(fit, 1)
```

The dashed horizontal line shows the cutoff threshold on the robust distances. Observations above the line are nominated as potential outliers by the BACON algorithm. It is left to the analyst to finally label outlying observations as such. In the next section, we introduce an alternative plotting method (see below).

The method `is_outlier()` returns a vector of logicals whether an observation has been flagged as a potential outlier.

```{r}
which(is_outlier(fit))
```

The (robust) center and covariance (scatter) matrix can be extracted with the auxiliary functions, respectively, `center()` and `cov()`.

```{r}
center(fit)
```

The robust Mahalanobis distances can be extracted with the `distance()` method.

### Philips data

Old television sets had a cathode ray tube with an electron gun. The emitted beam runs through a diaphragm that lets pass only a partial beam to the screen. The diaphragm consists of 9 components. The Philips data set contains $n = 667$ measurements on the $p = 9$ components (variables); see [Rousseeuw and van Driessen](#biblio) (1999).

The philips data can be loaded from the `R` package `cellWise` ([Raymaekers and Rousseeuw](#biblio), 2020). These data do not have sampling weights.


```{r}
data(data_philips, package = "cellWise")
head(data_philips)
```

We compute the BACON algorithm but this time with the initialization method `version = "V1"`.

```{r}
fit <- wBACON(data_philips, alpha = 0.05, version = "V1")
fit
```

The BACON algorithm detected `r sum(is_outlier(fit))` potential outliers. The robust (Mahalanobis) distances can be plotted against the univariate projection of the data, which maximizes the separation criterion of [Qiu and Joe](#biblio) (2006). This kind of diagnostic graph attempts to separate outlying from non-outlying observations as much as possible; see [Willems et al.](#biblio) (2009). It is helpful if the outliers are clustered. The graph is generated as follows.

```{r}
plot(fit, which = 2)
```

From the visual display, we see a cluster of potential outliers in the top right corner. The dashed horizontal line indicates the cutoff threshold on the distances as imposed by the BACON algorithm.

For very large datasets, the plot method can be called with the (additional) argument `hex = TRUE` to show a hexagonally binned scatterplot; see below. This plot method uses the functionality of the R package `hexbin` ([Carr et al.](#biblio), 2021).

```{r}
plot(fit, which = 2, hex = TRUE)
```

## Robust linear regression

The education data is on education expenditures in 50 US states in 1975 ([Chatterjee and Hadi](#biblio), 2012, Chap. 5.7). The data can be loaded from the `robustbase` package.

```{r}
data(education, package = "robustbase")
```

It is convenient to rename the variables.


```{r}
names(education)[3:6] <- c("RES", "INC", "YOUNG", "EXP")
head(education)
```

The measured variables for the 50 states are:

* `State`: State
* `Region`: group variable with outcomes: 1=Northeastern, 2=North central, 3=Southern, and 4=Western
* `RES`: Number of residents per thousand residing in urban areas in 1970
* `INC`: Per capita personal income in 1973 (\$US)
* `YOUNG`: Number of residents per thousand under 18 years of age in 1974
* `EXP`: Per capita expenditure on public education in a state (\$US), projected for 1975

### Model fit

We want to regress education expenditures (`EXP`) on the variables `RES`, `INC`, and `YOUNG` by the BACON algorithm, and obtain

```{r}
reg <- wBACON_reg(EXP ~ RES + INC + YOUNG, data = education)
reg
```

The instance `reg` is an object of the class `wbaconlm`.  The printed output of `wBACON_reg` is identical with the one of the `lm` function. In addition, we are told the size of the subset on which the regression has been computed. The observations not in the subset are considered outliers (here 1 out of 50 observations).

The `summary()` method can be used to obtain a summary of the estimated model.


```{r}
summary(reg)
```

The summary output of `wBACON_reg` is identical with the output of the `lm` estimate on the subset of outlier-free data,

```{r}
summary(lm(EXP ~ RES + INC + YOUNG, data = education[!is_outlier(reg), ]))
```

where we have used `is_outlier()` to extract the set of declared outliers from `reg` (the summary output of the `lm` estimate is not shown).

### Tuning

By default, `wBACON_reg` uses the parametrization $\alpha = 0.05$, `collect = 4`, and `version = "V2"`. These parameters are used to call the `wBACON` algorithm on the design matrix. Then, the same parameters are used to compute the robust regression.

To ensure a high breakdown point, `version = "V2"` should not be changed to `version = "V1"` unless you have good reasons.  The main "turning knob" to tune the algorithm is `alpha`, which defines the $(1-$`alpha`$)$ quantile of the Student $t$-distribution. All observations whose distances/discrepancies [See document `methods.pdf` in the folder `doc` of the package.] are smaller (in absolute value) than the quantile are selected into the subset of "good" data.  By choosing smaller values for `alpha` (e.g., 0.2), more observations are selected (ceteris paribus) into the subset of "good" data (and vice versa).

The parameter `collect` specifies the initial subset size, which is defined as $m = p \cdot collect$. It can be modified but should be chosen such that $m$ is considerably smaller than the number of observations $n$.  Otherwise there is a high risk of selecting too many "bad" observations into the initial subset, which will eventually bias the regression estimates.

In case the algorithm does not converge, we may increase the maximum number of iterations (default: `maxiter = 50`) and toggle `verbose = TRUE` to (hopefully) learn more why the method did not converge.

### Model diagnostics

The methods `coef()`, `vcov()`, and `predict()` work exactly the same as their `lm` counterparts. This is also true for the first three `plot` types, that is

* `which = 1`: Residuals vs Fitted,
* `which = 2`: Normal Q-Q,
* `which = 3`: Scale-Location


The plot types `4:6` of `plot.lm` are not implemented for objects of the class `wbaconlm` because it is not sensible to study the standard regression influence diagnostics in the presence of outliers in the model's design space. Instead, type four (`which = 4`) plots the robust Mahalanobis distances with respect to the non-constant design variables against the standardized residual. This plot has been proposed by [Rousseeuw and van Zomeren](#biblio) (1990).

```{r}
plot(reg, 4)
```

The *filled* circle(s) represent the outliers nominated by the BACON algorithm. The outlier in the top right corner is both a residual outlier and an outlier in the model's design space.

 * Observation with robust Mahalanobis distances larger than 4.57 (see abscissae) are flagged as outliers in the model's design space (leverage observations).
 * Observations whose standardized residual falls outside the interval spanned by $\pm \, t_{\alpha/(2m+2), m - p}$, where $t_{\alpha, m - p}$ is the $(1-\alpha)$ quantile of the Student $t$-distribution with $m-p$ degrees of freedom, $m$ denoting the size of the final subset of outlier-free data. Here, we have $m=49$, $\alpha = 0.05$ (see argument `alpha` of `wBACON_reg`), thus the interval is $[-3.52, \; 3.52]$.

---

## References {#biblio}

Béguin, C. and B. Hulliger (2002). *Robust Multivariate Outlier Detection and Imputation with Incomplete Survey Data*, Deliverable D4/5.2.1/2 Part C: EUREDIT project, https://www.cs.york.ac.uk/euredit/euredit-main.html, research project funded by the European Commission, IST-1999-10226.

Béguin, C. and B. Hulliger (2008). The BACON-EEM Algorithm for Multivariate Outlier Detection in Incomplete Survey Data, *Survey Methodology*, Vol. 34, No. 1, 91--103.

Billor, N., A. S. Hadi, and P. F. Vellemann (2000). BACON: Blocked Adaptive Computationally-efficient Outlier Nominators, *Computational Statistics and Data Analysis*, 34, 279--298.

Campbell, N. A. (1989). *Bushfire Mapping using NOAA AVHRR Data*. Technical Report. Commonwealth Scientific and Industrial Research Organisation, North Ryde.

Carr, D., N. Lewin-Koh, and M. Maechler (2021). *hexbin: Hexagonal Binning Routines*. R package version 1.28.2. (The package contains copies of lattice functions written by Deepayan Sarkar).

Chatterjee, S. and A. H. Hadi (2012). *Regression Analysis by Example*, 5th ed., Hoboken (NJ): John Wiley \& Sons.

Hulliger, B. and T. Schoch (2009). Robust multivariate imputation with survey data, in *Proceedings of the 57th Session of the International Statistical Institute*, Durban.

Hulliger, B. and M. Sterchi (2020). *modi: Multivariate Outlier Detection and Imputation for Incomplete Survey Data*, R package version 0.1-0.

Maechler, M., P. Rousseeuw, C. Croux, V. Todorov, A. Ruckstuhl, M. Salibian-Barrera, T. Verbeke, M. Koller, E. L. T. Conceicao, and M. Anna di Palma (2020). *robustbase: Basic Robust Statistics*, R package version 0.93-6.

Maronna, R. A. and V. J. Yohai (1995). The Behavior of the Stahel-Donoho Robust Multivariate Estimator, *Journal of the American Statistical Association*, 90, 330--341.

Qiu, W. and H. Joe (2006). Separation index and partial membership for clustering, *Computational Statistics and Data Analysis* 50, pp. 585-603.

Raymaekers, J. and P. Rousseeuw (2020). *cellWise: Analyzing Data with Cellwise Outliers*, R package version 2.2.1.

Rousseeuw, P. J. and K. van Driessen (1999). A fast algorithm for the Minimum Covariance Determinant estimator, *Technometrics*, 41, 212--223.

Rousseeuw, P. J. and K. van Zomeren (1990). Unmasking Multivariate Outliers and Leverage Points, *Journal of the American Statistical Association*, 411, 633--639.

Willems, G., H. Joe, and R. Zamar (2009). Diagnosing Multivariate Outliers Detected by Robust Estimators, *Journal of Computational and Graphical Statistics* 18, pp. 73-91.

## Notes {#notes}

<sup>1</sup> The `devtools` package can be installed from CRAN by `install.packages("devtools")`.

<sup>2</sup> The data are also distributed with the `R` package `robustbase` ([Maechler et al.](#biblio), 2020).

<sup>3</sup> The degrees of freedom $d$ is a function of the number of variables $p$, the number of observations $n$, and the size of the current subset $m$; see `methods.pdf` in the `inst/doc` folder of the package.

