dataGenerator: Data Generator In gfpop: Graph-Constrained Functional Pruning Optimal Partitioning

 dataGenerator R Documentation

Data Generator

Description

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

Usage

```dataGenerator(
n,
changepoints,
parameters,
type = "mean",
sigma = 1,
gamma = 1,
size = 10
)
```

Arguments

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

Value

a vector of size n generated by the chosen model

Examples

```dataGenerator(500, c(0.3, 0.6, 1), c(1, 2, 3), type = "mean", sigma = 0.5)

dataGenerator(1000, c(0.2, 0.33,0.5, 1), c(4, 0.2, 3,0.5), type = "variance")

dataGenerator(800, c(0.4, 0.8, 1), c(15, 5, 8), type = "mean", gamma  = 0.95, sigma = 0.4)

dataGenerator(400, c(0.4, 0.9, 1), c(2, 1.5, 3), type = "poisson")

dataGenerator(1000, c(0.44, 0.86, 1), c(0.5, 0.2, 0.4), type = "negbin", size = 3)
```

gfpop documentation built on March 18, 2022, 5:08 p.m.