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JSS Journal of Statistical Software
June 2014, Volume 58, Issue 3. http://www.jstatsoft.org/
changepoint: An RPackage for Changepoint Analysis
Rebecca Killick
Lancaster University
Idris A. Eckley
Lancaster University
Abstract
One of the key challenges in changepoint analysis is the ability to detect multiple
changes within a given time series or sequence. The changepoint package has been de-
veloped to provide users with a choice of multiple changepoint search methods to use in
conjunction with a given changepoint method and in particular provides an implementa-
tion of the recently proposed PELT algorithm. This article describes the search methods
which are implemented in the package as well as some of the available test statistics whilst
highlighting their application with simulated and practical examples. Particular empha-
sis is placed on the PELT algorithm and how results differ from the binary segmentation
approach.
Keywords: segmentation, break points, search methods, bioinformatics, energy time series, R.
1. Introduction
There is a growing need to be able to identify the location of multiple change points within
time series. However, as datasets increase in length the number of possible solutions to
the multiple changepoint problem increases combinatorially. Over the years several multiple
changepoint search algorithms have been proposed to overcome this challenge, most notably
the binary segmentation algorithm (Scott and Knott 1974;Sen and Srivastava 1975); the
segment neighborhood algorithm (Auger and Lawrence 1989;Bai and Perron 1998) and more
recently the PELT algorithm (Killick, Fearnhead, and Eckley 2012a). This paper describes
the changepoint package (Killick, Eckley, and Haynes 2014), available for R(RCore Team
2014) from the Comprehensive RArchive Network (CRAN) at http://CRAN.R-project.
org/package=changepoint. Package changepoint makes each of these algorithms available,
thus enabling users to select which method they would like to use for their analysis.
We are by no means the first to develop a changepoint package for the Renvironment. At
the time of writing several such packages exist, including those which provide a single test
statistic e.g., sde (Iacus 2009), bcp (Erdman and Emerson 2007) and/or are designed for a
2changepoint: An RPackage for Changepoint Analysis
specific (typically genomic) application e.g., cumSeg (Muggeo 2012), DNAcopy (Seshan and
Olshen 2008). More comprehensive Rpackages are also available such as strucchange (Zeileis,
Leisch, Hornik, and Kleiber 2002) for changes in regression and cpm (Ross 2013) for online
changepoint detection. However, all of the aforementioned packages implement a single search
method for detecting multiple changepoints. In contrast, the changepoint package uniquely
provides a choice of search algorithms for multiple changepoint detection in addition to a
variety of test statistics. In particular the package implements the search algorithms for a
selection of popular changepoint and penalty types. Specifically methods are implemented
for the change in mean and/or variance settings with a similar argument structure where
each function outputs an object of class ‘cpt’. Such an approach is deliberate to breed
familiarity and ease of use. Whilst the package is driven from these core functions, part of
our philosophy is to make it easier for others to use and adapt code snippets as appropriate.
To this end we have deliberately coded each part of a method in an individual function
which is also exported. Whilst several test statistics are included in the changepoint package
there are currently some notable gaps which are covered by other software. These include
changes in regression (see strucchange,Zeileis et al. 2002) and changes in autocorrelation
(see AutoPARM available from Davis, Lee, and Rodriguez-Yam 2006). In addition there is
currently no general software available whereby the user can supply their own cost function
and this would be an interesting avenue to pursue. A list of general changepoint software, and
indeed recent preprints in the area, are available from The Changepoint Repository (Killick,
Nam, Aston, and Eckley 2012b,http://changepoint.info).
The remainder of the paper is structured as follows. A brief background to changepoint
analysis is given in Section 2before Section 3describes the ‘cpt’ class and its methods.
Following this the three main functions; cpt.mean,cpt.var and cpt.meanvar are described
and explored using simulated and practical examples. In these sections particular emphasis
is placed on how to identify multiple changepoints and the difference between exact and
approximate methods. The paper is summarized in Section 7, where we provide a discussion.
2. Changepoint detection
This section begins by introducing the reader to changepoints through the single changepoint
problem before considering the extension to multiple changepoints. In its simplest form,
changepoint detection is the name given to the problem of estimating the point at which the
statistical properties of a sequence of observations change. Detecting such changes is impor-
tant in many different application areas. Recent examples include climatology (Reeves, Chen,
Wang, Lund, and Lu 2007), bioinformatic applications (Erdman and Emerson 2008), finance
(Zeileis, Shah, and Patnaik 2010), oceanography (Killick, Eckley, Jonathan, and Ewans 2010)
and medical imaging (Nam, Aston, and Johansen 2012).
More formally, let us assume we have an ordered sequence of data, y1:n= (y1, . . . , yn). A
changepoint is said to occur within this set when there exists a time, τ∈ {1, . . . , n −1},
such that the statistical properties of {y1, . . . , yτ}and {yτ+1, . . . , yn}are different in some
way. Extending this idea of a single changepoint to multiple changes, we will have a number
of changepoints, m, together with their positions, τ1:m= (τ1, . . . , τm). Each changepoint
position is an integer between 1 and n−1 inclusive. We define τ0= 0 and τm+1 =n, and
assume that the changepoints are ordered so that τi< τjif, and only if, i<j. Consequently
the mchangepoints will split the data into m+ 1 segments, with the ith segment containing
Journal of Statistical Software 3
data y(τi−1+1):τi. Each segment will be summarized by a set of parameters. The parameters
associated with the ith segment will be denoted {θi, φi}, where φiis a (possibly null) set of
nuisance parameters and θiis the set of parameters that we believe may contain changes.
Typically we want to test how many segments are needed to represent the data, i.e., how
many changepoints are present and estimate the values of the parameters associated with
each segment.
2.1. Single changepoint detection
Let us briefly recap the likelihood based framework for changepoint detection. Before con-
sidering the more general problem of identifying τ1:mchangepoint positions, we first consider
the identification of a single changepoint. The detection of a single changepoint can be posed
as a hypothesis test. The null hypothesis, H0, corresponds to no changepoint (m= 0) and
the alternative hypothesis, H1, is a single changepoint (m= 1).
We now introduce the general likelihood ratio based approach to test this hypothesis. The
potential for using a likelihood based approach to detect changepoints was first proposed by
Hinkley (1970) who derives the asymptotic distribution of the likelihood ratio test statistic
for a change in the mean within normally distributed observations. The likelihood based
approach was extended to changes in variance within normally distributed observations by
Gupta and Tang (1987). The interested reader is referred to Silva and Teixeira (2008) and
Eckley, Fearnhead, and Killick (2011) for a more comprehensive review.
A test statistic can be constructed which we will use to decide whether a change has occurred.
The likelihood ratio method requires the calculation of the maximum log-likelihood under
both null and alternative hypotheses. For the null hypothesis the maximum log-likelihood is
log p(y1:n|ˆ
θ), where p(·) is the probability density function associated with the distribution of
the data and ˆ
θis the maximum likelihood estimate of the parameters.
Under the alternative hypothesis, consider a model with a changepoint at τ1, with τ1∈
{1,2, . . . , n −1}. Then the maximum log likelihood for a given τ1is
ML(τ1) = log p(y1:τ1|ˆ
θ1) + log p(y(τ1+1):n|ˆ
θ2).(1)
Given the discrete nature of the changepoint location, the maximum log-likelihood value
under the alternative is simply maxτ1ML(τ1), where the maximum is taken over all possible
changepoint locations. The test statistic is thus
λ= 2 max
τ1
ML(τ1)−log p(y1:n|ˆ
θ).
The test involves choosing a threshold, c, such that we reject the null hypothesis if λ > c. If
we reject the null hypothesis, i.e., detect a changepoint, then we estimate its position as ˆτ1
the value of τ1that maximizes ML(τ1). The appropriate value for this parameter cis still an
open research question with several authors devising pvalues and other information criteria
under different types of changes. We refer the interested reader to Guyon and Yao (1999);
Chen and Gupta (2000); Lavielle (2005); Birge and Massart (2007) for interesting discussions
and suggestions for c.
It is clear that the likelihood test statistic can be extended to multiple changes simply by
summing the likelihood for each of the msegments. The problem becomes one of identifying
4changepoint: An RPackage for Changepoint Analysis
the maximum of ML(τ1:m) over all possible combinations of τ1:m. The following section
explores existing search methods that address this problem.
2.2. Multiple changepoint detection
With increased collection of time series and signal streams there is a growing need to be
able to efficiently and accurately estimate the location of multiple changepoints. This section
briefly introduces the main search methods available for identifying multiple changepoints
within the changepoint package. Arguably the most common approach to identify multiple
changepoints in the literature is to minimize
m+1
X
i=1 C(y(τi−1+1):τi)+βf (m) (2)
where Cis a cost function for a segment e.g., negative log-likelihood and βf(m) is a penalty
to guard against over fitting (a multiple changepoint version of the threshold c). This is
the approach which we adopt in this paper and the accompanying package. A brute force
approach to solve this minimization considers 2n−1solutions reducing to n−1
mif mis known.
The changepoint package implements three multiple changepoint algorithms that minimize
(2); binary segmentation (Edwards and Cavalli-Sforza 1965), segment neighborhoods (Auger
and Lawrence 1989) and the recently proposed pruned exact linear time (PELT) (Killick et al.
2012a). Each of these algorithms is briefly described in the following paragraphs, for more
information see the corresponding references.
At the time of writing binary segmentation is arguably the most widely used multiple change-
point search method and originates from the work of Edwards and Cavalli-Sforza (1965), Scott
and Knott (1974) and Sen and Srivastava (1975). Briefly, binary segmentation first applies a
single changepoint test statistic to the entire data, if a changepoint is identified the data is
split into two at the changepoint location. The single changepoint procedure is repeated on
the two new data sets, before and after the change. If changepoints are identified in either
of the new data sets, they are split further. This process continues until no changepoints are
found in any parts of the data. This procedure is an approximate minimization of (2) with
f(m) = mas any changepoint locations are conditional on changepoints identified previously.
Binary segmentation is thus an approximate algorithm but is computationally fast as it only
considers a subset of the 2n−1possible solutions. The computational complexity of the al-
gorithm is O(nlog n) but this speed can come at the expense of accuracy of the resulting
changepoints (see Killick et al. 2012a, for details).
The segment neighborhood algorithm was proposed by Auger and Lawrence (1989) and fur-
ther explored in Bai and Perron (1998). The algorithm minimizes the expression given by
Equation 2exactly using a dynamic programming technique to obtain the optimal segmenta-
tion for m+ 1 changepoints reusing the information that was calculated for mchangepoints.
This reduces the computational complexity from O(2n) for a naive search to O(Qn2) where
Qis the maximum number of changepoints to identify. Whilst this algorithm is exact, the
computational complexity is considerably higher than that of binary segmentation.
The binary segmentation and segment neighborhood algorithms would appear to indicate a
trade-off between speed and accuracy however this need not be the case. The PELT algorithm
proposed by Killick et al. (2012a) is similar to that of the segment neighborhood algorithm
in that it provides an exact segmentation. However, due to the construction of the PELT
Journal of Statistical Software 5
algorithm, it can be shown to be more computationally efficient, due to its use of dynamic
programming and pruning which can result in an O(n) search algorithm subject to certain
assumptions being satisfied, the majority of which are not particularly onerous. Indeed the
main assumption that controls the computational time is that the number of changepoints
increases linearly as the data set grows, i.e., changepoints are spread throughout the data
rather than confined to one portion.
All three search algorithms are available within the changepoint package. The following
sections introduce the structure of the package, its S4 class – ‘cpt’ and the core functions
that enable quick and efficient analysis of changepoint problems.
3. Introduction to the package and the ‘cpt’ class
The changepoint package introduces a new object class called ‘cpt’ to store changepoint anal-
ysis objects. This section provides an introduction to the structure and methods associated
with the ‘cpt’ class, together with examples of its specific use.
Each of the core functions outputs an object of the ‘cpt’S4 class. The class has been
constructed such that the ‘cpt’ object contains the main features required for a changepoint
analysis and future summaries. Each of these is stored within a slot entry in the ‘cpt’ class.
The slots within the class are,
data.set – a time series (‘ts’) object containing the numeric values of the data;
cpttype – characters describing the type of changepoint sought e.g., mean, variance;
method – characters denoting the single or multiple changepoint search method applied;
test.stat – characters denoting the test statistic, i.e., assumed distribution / distribution-
free method;
pen.type – characters denoting the penalty type, e.g., AIC, BIC, manual;
pen.value – the numeric value of the penalty used in the analysis;
cpts – a numeric vector giving the estimated changepoint locations always ending in n,
the length of the time series in the data.set slot;
ncpts.max – the numeric maximum number of changepoints searched for, e.g., 1, 5, Inf
and denoted Qin Section 2;
param.est – a list of parameters where each element in the list is a vector of the
estimated numeric parameter values for each segment, denoted θiin Section 2;
date – the system time / date when the analysis was performed.
Slots of an S4 object are typically accessed using the @ symbol (in contrast to the $ for S3
objects). Whilst this is still possible in the changepoint package, we have created accessor and
replacement functions to control the access and replacement of slots. The accessor functions
are simply the slot names. For example data.set(x) displays the vector of data contained
within the ‘cpt’ object x. The class slots are automatically populated with the correct infor-
mation obtained from the completed analysis. Feedback from trials with the package users
6changepoint: An RPackage for Changepoint Analysis
indicate that the accessor and replacement functions aid ease-of-use for those unfamiliar with
S4 classes. Further demonstration of how the accessor and replacement functions work in
practice are given in the examples within each section.
In addition to accessor and replacement functions, the changepoint package also contains a
couple of extra functions that a user may find useful. The first of these is the ncpts function
which, given a ‘cpt’ object from a changepoint analysis, returns the number of identified
changepoints. This can be particularly useful if the number of changepoints is expected to be
large and/or users wish to quickly check whether the returned number of changepoints is equal
to the maximum searched for when using the binary segmentation or segment neighborhood
search algorithms. Similarly the second additional function, seg.len, returns the size of
the segments, i.e., how many observations there are between consecutive changepoints. This
may be useful when performing a changepoint analysis as short segments can be used as an
indicator that the penalty function may be set too low.
All the functions described above are related to the ‘cpt’ class within the changepoint package.
The following section reviews the methods that act on the ‘cpt’ class.
3.1. Methods within the ‘cpt’ class
The methods associated with the ‘cpt’ class are summary,print,plot,coef and logLik.
The summary and print methods display standard information about the ‘cpt’ object. The
summary function displays a synopsis of the results from the analysis including number of
changepoints and, where this number is small, the location of those changepoints. In contrast,
the print function prints details pertaining to the S4 class including slot names and when
the S4 object was created.
Having performed a changepoint analysis, it is often helpful to be able to plot the changepoints
on the original data to visually inspect whether the estimated changepoints are reasonable. To
this end we include a plot method for the ‘cpt’ class. The method adapts to the assumed type
of changepoint, providing a different output dependent on the type of change. For example, a
change in variance is denoted by a vertical line at the changepoint location whereas a change
in mean is indicated by horizontal lines depicting the mean value in different segments.
Similarly once a changepoint analysis has been conducted one may wish to retrieve the param-
eter values for each segment or the log likelihood for the fitted data. These can be obtained
using the standard coef and logLik generics; examples are given in the code detailed below.
The following sections explore the use of the core functions within the changepoint package.
We begin in Section 4by demonstrating the key steps to a changepoint analysis via the
cpt.mean function. Sections 5and 6utilize the steps in the change in mean analysis to
explore changes in variance and both mean and variance respectively.
4. Changes in mean: The cpt.mean function
Early work on changepoint problems focused on identifying changes in mean and includes the
work of Page (1954) and Hinkley (1970) who created the likelihood ratio and cumulative sum
(CUSUM) test statistics respectively.
Within the changepoint package all change in mean methods are accessed using the cpt.mean
function. The function is structured as follows:
Journal of Statistical Software 7
cpt.mean(data, penalty = "SIC", pen.value = 0, method = "AMOC", Q = 5,
test.stat = "Normal", class = TRUE, param.estimates = TRUE)
The arguments within this function are:
data – A vector or ‘codets’ object containing the data within which to find a change
in mean. If multiple datasets require to be analyzed, then this can be a matrix where
each row is considered a separate dataset.
penalty – Choice of "None","SIC","BIC","AIC","Hannan-Quinn","Asymptotic"
and "Manual" penalties. If "Manual" is specified, the manual penalty is contained in
pen.value. If "Asymptotic" is specified, the theoretical type I error is contained in
pen.value. The predefined penalties listed do not count the changepoint as a parame-
ter, postfix a 1 e.g., "SIC1" to count the changepoint as a parameter.
pen.value – The theoretical type I error e.g., 0.05 when using the "Asymptotic"
penalty. Alternatively when using the "Manual" penalty it is a numeric value or text
which when evaluated results in a penalty value.
method – Single or multiple changepoint method. Choice of "AMOC" (at most one
change), "PELT","SegNeigh" or "BinSeg". Default is "AMOC". See Section 2for further
details of methods.
Q– When using the "BinSeg" method this is the maximum number of changepoints
to search for. When using the "SegNeigh" method this is the maximum number of
segments (number of changepoints + 1) to search for. This is not required for the
"PELT" method as this automatically selects the number of segments.
test.stat – The test statistic, i.e., assumed distribution or distribution-free method
for data. Choice of "Normal" or "CUSUM". The test statistics behind the distributional
options are contained within Hinkley (1970) for the "Normal" option and Page (1954)
for the "CUSUM" option.
class – Logical. If TRUE then an object of class ‘cpt’ is returned.
param.estimates – Logical. If TRUE and class = TRUE then parameter estimates are
returned. If FALSE or class = FALSE no parameter estimates are returned.
Briefly the search options consist of exact methods: PELT (O(n) if assumptions are sat-
isfied), segment neighborhoods (O(Qn2)); and approximate methods: binary segmentation
(O(nlog n)). Further details of the search options in the method argument are given in Sec-
tion 2.
Several standard penalty functions used within changepoint analysis have been included in
this function. These are: SIC (Schwarz information criterion), BIC (Bayesian information
criterion), AIC (Akaike information criterion) and Hannan-Quinn. The authors will seek to
include further penalty functions, such as minimum description length (MDL) (Davis et al.
2006), in future versions of the package. The user can also enter a manual penalty value by
numeric value or formula. An example of using a manual penalty value with a formula is given
in Section 4.1. In addition to the standard Rfunctions, the following variables are available
for the user to utilize:
8changepoint: An RPackage for Changepoint Analysis
tau – the proposed changepoint location (only available when using "AMOC");
null – the likelihood under the null model of no changepoint (only available when using
"AMOC");
alt – the likelihood under the alternative model of a single changepoint (only available
when using "AMOC");
diffparam – the difference in the number of parameters between the no changepoint
and single changepoint model e.g., for a Normal distribution, 1 for a change in mean or
variance and 2 for a change in both mean and variance;
n– the length of the data.
Thus if one wanted to use a penalty based on the ratio of the lengths of data before and
after the change, then one may use penalty = "Manual",pen.value = "tau / (n - tau)".
Note this is only possible using "AMOC".
The remainder of this section gives a worked example exploring how to identify a change in
mean.
4.1. Example: Changes in mean
We now describe the general structure of a changepoint analysis using the changepoint pack-
age. We begin by demonstrating the various possible stages within a change in mean analysis.
To this end we simulate a dataset (m.data) of length 400 with multiple changepoints at 100,
200, 300. The sequence has four segments and the means for each segment are 0, 1, 0, 0.2.
R> library("changepoint")
R> set.seed(10)
R> m.data <- c(rnorm(100, 0, 1), rnorm(100, 1, 1), rnorm(100, 0, 1),
+ rnorm(100, 0.2, 1))
R> ts.plot(m.data, xlab = "Index")
Imagine that we have been presented with this dataset and are asked to perform a changepoint
analysis. The first question we aim to answer is“Is there a change within the data?”. Our first
choice in answering this question is whether we wish to consider a single change or whether
multiple changes are plausible. From a visual inspection of the data in Figure 1(a), we suspect
multiple changes in mean may exist.
The challenge in multiple changepoint detection is identifying the optimal number and location
of changepoints as the number of solutions increases rapidly with the size of the data. In this
example where n= 400, we have 399 possible solutions for a single changepoint, for two
changes there are 79401 possible solutions and this is not taking into account that we do not
know how many changes there are! As such it is clearly desirable to use an efficient method
for searching the large solution space.
Any of the three search methods could be used to detect these changes. For this example we
will compare the PELT and binary segmentation search methods as this provides a comparison
between exact and alternative algorithms (see Section 2). For now we will assume that the
dataset is independent and Normally distributed and consider an alternative towards the end
of this section.
Journal of Statistical Software 9
R> m.pelt <- cpt.mean(m.data, method = "PELT")
R> plot(m.pelt, type = "l", cpt.col = "blue", xlab = "Index",
+ cpt.width = 4)
R> cpts(m.pelt)
[1] 97 192 273 353 362 366
R> m.binseg <- cpt.mean(m.data, method = "BinSeg")
R> plot(m.binseg, type = "l", xlab = "Index", cpt.width = 4)
R> cpts(m.binseg)
[1] 79 99 192 273
In this case, where we use the default SIC penalty, the cpts function returned 6 changepoints
(97, 192, 273, 353, 362, 366) for PELT and 4 changepoints (79, 99, 192, 273) for binary
segmentation. By construction we know that there are three changepoints within the dataset.
We can either believe that there are six/four changes or consider that the method is too
sensitive and try to compensate by increasing the penalty. The choice of appropriate penalty
is still an open question and typically depends on many factors including the size of the
changes and the length of segments, both of which are unknown prior to analysis (see Guyon
and Yao 1999;Lavielle 2005;Birge and Massart 2007). As new approaches to penalty choice
become available we will seek to include them within the changepoint package. In current
practice, the choice of penalty is often assessed by plotting the data and changepoints to see
if they seem reasonable.
Figure 1(b) shows the m.pelt changepoints. Note that there are two changes towards the end
of the dataset which have very small segments. These are plausibly artifacts of the data rather
than true changes in the underlying process. In an effort to remove these seemingly spurious
changepoints we can increase the penalty to 1.5 * log(n) rather than log(n) (SIC). This
change is achieved by changing the penalty type to "Manual" and setting the value argument
to "1.5 * log(n)". Figure 1(d) shows the result which seem more plausible.
R> m.pm <- cpt.mean(m.data, penalty = "Manual", pen.value = "1.5 * log(n)",
+ method = "PELT")
R> plot(m.pm, type = "l", cpt.col = "blue", xlab = "Index", cpt.width = 4)
R> cpts(m.pm)
[1] 97 192 273
On the other hand, if we only consider the changepoints identified by the binary segmentation
algorithm in Figure 1(c) then we may plausibly believe that there are four changes within
the data as the spurious segment is much larger. However, for comparison we also perform
the analysis with the increased penalty and find that the changepoints identified remain the
same.
R> m.bsm <- cpt.mean(m.data, "Manual", pen.value = "1.5 * log(n)",
+ method = "BinSeg")
R> cpts(m.bsm)
10 changepoint: An RPackage for Changepoint Analysis
Index
m.data
0 100 200 300 400
−2 −1 0 1 2 3
(a) m.data.
Index
data.set.ts(x)
0 100 200 300 400
−2 −1 0 1 2 3
(b) PELT changepoints with default penalty.
Index
data.set.ts(x)
0 100 200 300 400
−2 −1 0 1 2 3
(c) Binary segmentation changepoints with de-
fault penalty.
Index
data.set.ts(x)
0 100 200 300 400
−2 −1 0 1 2 3
(d) PELT changepoints with manual penalty.
Figure 1: Plot of the simulated dataset m.data along with horizontal lines for the underlying
(fitted) mean.
[1] 79 99 192 273
Recall from Section 2that both the segment neighborhood and PELT algorithms are exact.
Thus, for a linear penalty, the only difference between them is their computational time. A
user can use the below commands on their own computer to identify their personal speedup
for this example.
R> system.time(cpt.mean(m.data, method = "SegNeigh"))
R> system.time(cpt.mean(m.data, method = "PELT"))
Using modern computers for this example PELT will return a time needed of 0.001 or 0.002
seconds compared to segment neighborhoods where the authors have seen a range from 0.4
to 1.1 seconds for the time needed.
Journal of Statistical Software 11
As a final note on this example, if the Normal assumption made at the start of the analysis
is questionable then the CUSUM method, which has no distributional assumptions, can be
used by adding the argument test.stat = "CUSUM".
Thus far we have only considered a simulated example. In the next section we apply the
cpt.mean function to some Glioblastoma data previously analyzed by Lai, Johnson, Kucher-
lapati, and Park (2005).
4.2. Case study: Glioblastoma
Lai et al. (2005) compare different methods for segmenting array comparative genomic hy-
bridization (aCGH) data from Glioblastoma multiforme (GBM), a type of brain tumor. These
arrays were developed to identify DNA copy number alteration corresponding to chromosomal
aberrations. High-throughput aCGH data are intensity ratios of diseased vs. control samples
indexed by the location on the genome. Values greater than 1 indicate diseased samples have
additional chromosomes and values less than 1 indicate fewer chromosomes. Detection of
these aberrations can aid future screening and treatments of diseases.
The example we consider is from Figure 4 in Lai et al. (2005), the data is replicated in the
changepoint package for ease. Following Lai et al. (2005) we fit a Normal distribution with a
piecewise constant mean using a likelihood criterion. Figure 2demonstrates that PELT (with
default penalty) gives the same segmentation as the CGHseg method from Lai et al. (2005).
R> data("Lai2005fig4", package = "changepoint")
R> Lai.default <- cpt.mean(Lai2005fig4[, 5], method = "PELT")
R> plot(Lai.default, pch = 20, col = "grey", cpt.col = "black", type = "p",
+ xlab = "Index")
R> cpts(Lai.default)
[1] 81 85 89 96 123 133
R> coef(Lai.default)
$mean
[1] 0.2468910 4.6699210 0.4495538 4.5902489 0.2079891 4.2913844 0.2291286
5. Changes in variance: The cpt.var function
Whilst considerable research effort has been given to the change in mean problem, Chen and
Gupta (1997) observe that the detection of changes in variance has received comparatively
little attention. Much of the work in this area builds on the foundational work of Hinkley
(1970) in the change in mean setting. See for example Hsu (1979), Horvath (1993) and Chen
and Gupta (1997) who extend Hinkley’s ideas to the change in variance setting. Existing
methods within the change in variance literature find it hard to detect subtle changes in
variability, see Killick et al. (2010).
Within the changepoint package all change in variance methods are accessed using the cpt.var
function. The function is structured as follows:
12 changepoint: An RPackage for Changepoint Analysis
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Index
data.set.ts(x)
0 50 100 150 200
−2 0 2 4
Figure 2: Plot of the GBM data along with horizontal lines for the underlying mean.
cpt.var(data, penalty, pen.value, know.mean = FALSE, mu = NA, method, Q,
test.stat = "Normal", class, param.estimates)
The data,penalty,pen.value,method,Q,class and param.estimates arguments are the
same as for the cpt.mean function (see Section 4). The three remaining arguments are
interpreted as follows.
know.mean – This logical argument is only required for test.stat = "Normal". If TRUE
then the mean is assumed known and mu is taken as its value. If FALSE and mu = NA
(default value) then the mean is estimated via maximum likelihood. If FALSE and the
value of mu is supplied, mu is not estimated but is counted as an estimated parameter
for decisions.
mu – Only required for test.stat = "Normal". Numerical value of the true mean of
the data (if known). Either single value or vector of length nrow(data). If data is a
matrix and mu is a single value, the same mean is used for each row.
test.stat – The test statistic, i.e., assumed distribution or distribution-free method
for data. Choice of "Normal" or "CSS". The test statistics behind the distributional
options are contained within Chen and Gupta (2000) for the "Normal" option and Chen
and Gupta (1997) for the "CSS" option.
The remainder of this section is a worked example considering changes in variability within
wind speeds.
Journal of Statistical Software 13
5.1. Case study: Irish wind speeds
With the increase of wind based renewables in the power grid, there has become great interest
in forecasting wind speeds. Often modelers assume a constant dependence structure when
modeling the existing data before producing a forecast. Here we conduct a naive changepoint
analysis of wind speed data which are available in the Rpackage gstat (Pebesma 2004).
The data provided are daily wind speeds from 12 meteorological stations in the Republic of
Ireland. The data has previously been analyzed by several authors including Haslett and
Raftery (1989) and Gneiting, Genton, and Guttorp (2007). These analyses were concerned
with a spatial-temporal model for 11 of the 12 sites. Here we consider a single site, Claremorris
depicted in Figure 3.
R> data("wind", package = "gstat")
R> ts.plot(wind[, 11], xlab = "Index")
The variability of the data appears smaller in some sections and larger in others, this motivates
a search for changes in variability. Wind speeds are by nature diurnal and thus have a periodic
mean. The change in variance approaches within the cpt.var function require the data to
have a fixed value mean over time and thus this periodic mean must be removed prior to
analysis. Whilst there are a range of options for removing this mean, we choose to take first
differences as this does not require any modeling assumptions. Following this we assume that
the differences follow a Normal distribution with changing variance and thus use the cpt.var
function. Again we compare the analyses provided by the PELT and binary segmentation
algorithms.
R> wind.pelt <- cpt.var(diff(wind[, 11]), method = "PELT")
R> plot(wind.pelt, xlab = "Index")
R> logLik(wind.pelt)
-like -likepen
37328.68 37856.13
R> wind.bs <- cpt.var(diff(wind[, 11]), method = "BinSeg")
Warning message:
In binseg.var.norm(coredata(data), Q, pen.value, know.mean, mu) :
The number of changepoints identified is Q, it is advised to increase Q to
make sure changepoints have not been missed.
R> ncpts(wind.bs)
[1] 5
Note that unlike the PELT algorithm, the binary segmentation algorithm has only found
5 changepoints. This is because we used the default value of the parameters that set Q
= 5 which results in a maximum of 5 changepoints identified. Whilst a warning message
is produced, when performing an analysis using binary segmentation this should always be
checked and the default increased if necessary.
14 changepoint: An RPackage for Changepoint Analysis
R> wind.bs <- cpt.var(diff(wind[, 11]), method = "BinSeg", Q = 60)
R> plot(wind.bs, xlab = "Index")
R> ncpts(wind.bs)
[1] 8
R> logLik(wind.bs)
-like -likepen
37998.37 38068.69
As we are considering the negative log-likelihood the smaller value provided by PELT is
preferred. Even when eye-balling the results, it would appear that the PELT segmentation is
more appropriate than that of the binary segmentation analysis, see Figure 3.
6. Changes in mean and variance: The cpt.meanvar function
The changepoint package contains four distributional choices for a change in both the mean
and variance; Exponential, Gamma, Poisson and Normal. The Exponential, Gamma and
Poisson distributional choices only require a change in a single parameter to change both
the mean and the variance. In contrast, the Normal distribution requires a change in two
parameters. The multiple parameter changepoint problem has been considered by many
authors including Horvath (1993) and Picard, Robin, Lavielle, Vaisse, and Daudin (2005).
Each distributional option is available within the cpt.meanvar function which has a similar
structure to the cpt.mean and cpt.var functions from previous sections. The basic call
format is as follows:
cpt.meanvar(data, penalty, pen.value, method, Q, test.stat = "Normal", class,
param.estimates, shape = 1)
The data,penalty,pen.value,method,Q,class and param.estimates arguments are the
same as those described for the cpt.mean function (see Section 4). The remaining arguments
are interpreted as follows.
test.stat – The test statistic, i.e., assumed distribution of data. Choice of "Normal",
"Gamma","Exponential" or "Poisson".
shape – Value of the known shape parameter required when test.stat = "Gamma".
Following the format of previous sections we briefly describe a case study using data on notable
inventions / discoveries.
6.1. Case study: Discoveries
This section considers the dataset called discoveries available within the datasets package in
the base distribution of R. The data are the counts of the number of “great” inventions and/or
scientific discoveries in each year from 1860 to 1959. Our approach models each segment as
following a Poisson distribution with its own rate parameter. Again we compare the results
for both PELT and binary segmentation search methods.
Journal of Statistical Software 15
Index
wind[, 11]
0 1000 2000 3000 4000 5000 6000
0 5 10 15 20 25 30
(a)
Index
data.set.ts(x)
0 1000 2000 3000 4000 5000 6000
−20 −10 0 10 20
(b)
Index
data.set.ts(x)
0 1000 2000 3000 4000 5000 6000
−20 −10 0 10 20
(c)
Figure 3: (a) Republic of Ireland hourly wind speeds, (b) and (c) show the first differences of
(a) with vertical lines depicting changepoints identified by (b) PELT and (c) binary segmen-
tation.
R> data("discoveries", package = "datasets")
R> dis.pelt <- cpt.meanvar(discoveries, test.stat = "Poisson",
+ method = "PELT")
R> plot(dis.pelt, cpt.width = 3)
R> cpts.ts(dis.pelt)
[1] 1883 1888 1932 1952
16 changepoint: An RPackage for Changepoint Analysis
Time
data.set.ts(x)
1860 1880 1900 1920 1940 1960
0 2 4 6 8 10 12
Figure 4: Discoveries dataset with identified changepoints.
R> dis.bs <- cpt.meanvar(discoveries, test.stat = "Poisson",
+ method = "BinSeg")
R> cpts.ts(dis.bs)
[1] 1883 1888 1932 1952
The number and year of the changepoints identified by both methods are the same. Here
we have used the cpts.ts function to return the date of the changepoints rather than their
position within the sequence of data.
7. Summary
The unique contribution of the changepoint package is that the user has the ability to select
the multiple changepoint search method for analysis. The package contains three such meth-
ods: segment neighborhood; binary segmentation and PELT and this paper has described
and demonstrated some differences between these approaches. The multiple changepoint
search methods are available both for changes in mean and/or variance using distributional
or distribution-free assumptions utilizing both established and novel methods. As such the
changepoint package is useful both for practitioners to implement existing methods and for
researchers to compare the performance of new approaches against the established literature.
Acknowledgments
The authors wish to thank Paul Fearnhead for helpful discussions and encouragement as they
developed this work as well as the editor and anonymous referees for helpful feedback on
Journal of Statistical Software 17
earlier versions of this manuscript. R. Killick and I.A. Eckley acknowledge financial support
from Shell Research Limited and the Engineering and Physical Sciences Research Council
(EPSRC).
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Affiliation:
Rebecca Killick
Department of Mathematics & Statistics
Lancaster University
LA1 4YF, United Kingdom
E-mail: r.killick@lancs.ac.uk
URL: http://www.lancs.ac.uk/~killick/
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 58, Issue 3 Submitted: 2013-01-10
June 2014 Accepted: 2014-02-23
