# plot.gfpop: plot.gfpop In gfpop: Graph-Constrained Functional Pruning Optimal Partitioning

 plot.gfpop R Documentation

## plot.gfpop

### Description

Plotting inferred segment parameters (the result of gfpop) and data.

### Usage

```## S3 method for class 'gfpop'
plot(x, ..., data, multiple = TRUE)
```

### Arguments

 `x` a gfpop class object `...` Other parameters `data` the data from which we get the gfpop result `multiple` if `TRUE` we plot data and the model on different graphs. Only with `"mean"` and `"poisson"` cost functions (as in that case the parameter values represent the data mean value over each segment) we allow the User to plot signal and data on a single graph.

### Value

plot data and the inferred gfpop segments

### Examples

```n <- 1000 #data length
data <- dataGenerator(n, c(0.3, 0.4, 0.7, 0.95, 1), c(1, 3, 1, -1, 4), "mean", sigma = 3)
myGraph <- graph(type = "relevant", gap = 0.5, penalty = 2 * sdDiff(data) ^ 2 * log(n))
g <- gfpop(data, myGraph, type = "mean")
plot(x = g, data = data, multiple = FALSE)

data <- dataGenerator(n, c(0.4, 0.8, 1), c(1, 1.7, 2.3), "exp")
g <- gfpop(data,graph(type = "isotonic", penalty = 2 * sdDiff(data) ^ 2 * log(n)), type = "exp")
plot(x = g, data = data, multiple = TRUE)

data <- dataGenerator(n, c(0.22, 0.75, 1), c(1.4,1,0.8), "poisson")
g <- gfpop(data, paperGraph(8), type = "poisson")
plot(x = g, data = data, multiple = TRUE)
```

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