`dpseg`

: piecewise linear segmentation by a simple
dynamic programing algorithmauthors: “Rainer Machne, Peter F. Stadler”

This package performs piecewise linear segmentation of ordered data
by a dynamic programing algorithm, provided via the function
`dpseg`

. It was developed for time series data, eg. growth
curves, and for genome-wide read-count data from next generation
sequencing.

The package and its documentation are also intended to serve as a
tutorial on dynamic programing and the segmentation problem. A
`movie`

function visualizes the progress of the algorithm
through the data.

Moreover, the package features generic implementations of dynamic
programing routines, where new segmentation criteria (“scoring
functions”) can be tested in base `R`

and efficient versions
implemented in `Rcpp`

.

See the package vignette (`vignette("dpseg")`

) for
details.

Install package from within `R`

via `cran`

:

`install.packages("dpseg")`

```
library(devtools)
install_gitlab("raim/dpseg")
```

```
library(dpseg)
# get example data `oddata` - bacterial growth measured as optical density OD
x <- oddata$Time
y <- log(oddata[,"A2"]) # note: exponential growth -> log(y) is linear
segs <- dpseg(x=x, y=y, jumps=FALSE, P=0.0004)
## inspect resulting segments
print(segs)
## plot results
plot(segs)
## use predict method
lines(predict(segs),lty=2, lwd=3, col="yellow")
## view the algorithm in action
movie(segs)
```

The problem is to find break-points in 2-dimensional data, eg.
timeseries, that split the data into linear segments. This can be
formulated as an optimization problem that can be solved by
`dynamic programing`

:

```
\newcommand{\Ell}{\mathcal{L}}
\newcommand{\jump}{\mathcal{J}}
\newcommand{\Var}{\mathrm{Var}}
\newcommand{\Cov}{\mathrm{Cov}}
\newcommand{\lmax}{\ell_\text{max}}
\newcommand{\lmin}{\ell_\text{min}}
S_j = \max_{i\le j} (S_{i-\jump} + \text{score}(i,j)) - P\;,
```

where the \(`\text{score}`\) quantifies how well a segment between points \(`i`\) and \(`j`\) is defined, eg. some goodness-of-fit measure such as the negative variance of the residuals

`\text{score}(i,j) = -s_r^2`

of a straight line fitted through data points from points \(`i`\) to \(`j`\). \(`P`\) is a penalty term, and \(`P>0`\) allows to fine-tune segment lengths. At constant scores it will accumulate in \(`S_j`\) (subtracted for each \(`i`\)), forcing the algorithm to “wait” until a higher score is reached. Similarly, initialization of \(`S_0>0`\) to a relatively large value avoids spurious segments of length 1 at \(`j=1`\) by enforcing a break-point before \(`i=1`\). \(`S_1`\) has to be initialized to \(`S_1=-P`\).

Discontinuous jumps between adjacent segments can be allowed with \(`\newcommand{\jump}{\mathcal{J}} \jump =1`\), while segment borders (break-points) are part of both left and right segments with \(`\newcommand{\jump}{\mathcal{J}} \jump =0`\), (in which case \(`S_0`\) has no effect).

See the package vignette (`vignette("dpseg")`

) for
details.