PoissonLoss

Compute the weighted Poisson loss function, which is <code>seg.mean</code> -
<code>count</code> * log(<code>seg.mean</code>). The edge case is when the mean is zero, in
which case the probability mass function takes a value of 1 when
the data is 0 (and 0 otherwise). Thus the log-likelihood of a
maximum likelihood segment with mean zero must be zero.

Computes optimal changepoint models using the
Poisson likelihood for non-negative count data,
subject to the PeakSeg constraint:
the first change must be up, second change down, third change up, etc.
For more info about the models and algorithms,
read "A log-linear time algorithm for constrained changepoint detection"
<arXiv:1703.03352> by TD Hocking et al.

Toby Hocking

PeakSegOptimal

Optimal Segmentation Subject to Up-Down Constraints

PoissonLoss function

<dl> <dt>count</dt>
<dd>count</dd> <dt>seg.mean</dt>
<dd>seg.mean</dd> <dt>weight</dt>
<dd>weight</dd></dl>

Arguments

Author

PoissonLoss — PoissonLoss

<dl>

 <dt>count</dt>
<dd>count</dd>

 <dt>seg.mean</dt>
<dd>seg.mean</dd>

 <dt>weight</dt>
<dd>weight</dd>

</dl>

PoissonLoss: PoissonLoss

Description

Usage

Arguments

Author

Examples