dataGenerator

Generating data with a given model = changepoint relative positions + parameters + type of cost + (standard deviation + gamma decay)

Penalized parametric change-point detection by functional pruning dynamic programming algorithm. The successive means are constrained using a graph structure with edges defining the nature of the changes These changes can be unconstrained (type std), up or down constrained (type up and down) or constrained by a minimal size jump (type abs). The type null means that the graph allows us to stay on the same segment. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a) for biweight (K) and Huber losses (K and a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). These costs should have a quadratic, log-linear or a log-log representation. This includes quadratic Gaussian cost (type = 'mean'), log-linear cost (type = 'variance', 'poisson' or 'exp') and log-log cost (type = 'negbin'). More details in the paper published in the Journal of Statistical Software: <doi:10.18637/jss.v106.i06>.

Vincent Runge

gfpop

Graph-Constrained Functional Pruning Optimal Partitioning

Toby Hocking

Guillem Rigaill

Daniel Grose

Gaetano Romano

Fatemeh Afghah

Paul Fearnhead

Michel Koskas

Arnaud Liehrmann

dataGenerator function

<dl><dt>n</dt>
<dd>number of data points to generate</dd>
<dt>changepoints</dt>
<dd>vector of positions of the changepoints in (0,1] (last element is always 1).</dd>
<dt>parameters</dt>
<dd>vector of means for the consecutive segments (same length as changepoints)</dd>
<dt>type</dt>
<dd>a string defining the cost model to use: <code>"mean"</code>, <code>"variance"</code>, <code>"poisson"</code>, <code>"exp"</code>, <code>"negbin"</code></dd>
<dt>sigma</dt>
<dd>a positive number = the standard deviation of the data</dd>
<dt>gamma</dt>
<dd>a number between 0 and 1 : the coefficient of the exponential decay (by default = 1 for piecewise constant signals)</dd>
<dt>size</dt>
<dd>parameter of the <code>rnbinom</code> function</dd></dl>

Arguments

Data Generator — dataGenerator

<dl>

<dt>n</dt>
<dd>number of data points to generate</dd>


<dt>changepoints</dt>
<dd>vector of positions of the changepoints in (0,1] (last element is always 1).</dd>


<dt>parameters</dt>
<dd>vector of means for the consecutive segments (same length as changepoints)</dd>


<dt>type</dt>
<dd>a string defining the cost model to use: <code>"mean"</code>, <code>"variance"</code>, <code>"poisson"</code>, <code>"exp"</code>, <code>"negbin"</code></dd>


<dt>sigma</dt>
<dd>a positive number = the standard deviation of the data</dd>


<dt>gamma</dt>
<dd>a number between 0 and 1 : the coefficient of the exponential decay (by default = 1 for piecewise constant signals)</dd>


<dt>size</dt>
<dd>parameter of the <code>rnbinom</code> function</dd>

</dl>

Data Generator

dataGenerator: Data Generator

Description

Usage

Value

Arguments

Examples