# est.net: Sparse network estimation using non-negative matrix... In fabisearch: Change Point Detection in High-Dimensional Time Series Networks

 est.net R Documentation

## Sparse network estimation using non-negative matrix factorization (NMF) for data between change points

### Description

This function estimates sparse networks using non-negative matrix factorization (NMF) for data between change points.

### Usage

```est.net(
Y,
lambda,
nruns = 50,
rank = "optimal",
algtype = "brunet",
changepoints = NULL
)
```

### Arguments

 `Y` An input multivariate time series in matrix format, with variables organized in columns and time points in rows. All entries in Y must be positive. `lambda` A positive real number, which defines the clustering method and/or the cutoff value when estimating an adjacency matrix from the computed consensus matrix. If lambda = a positive integer value, say 6, complete-linkage, hierarchical clustering is applied to the consensus matrix and the cutoff is at 6 clusters. If lambda is a vector of positive integer values, say c(4, 5, 6), the same clustering method is applied for each value sequentially. If lambda = a positive real number, say 0.5, entries in the consensus matrix with a value greater than or equal to 0.5 are labeled 1, while entries less than 0.5 are labeled 0. Similarly, if lambda is a vector of positive real numbers, say c(0.1, 0.3, 0.8), the same thresholding method is applied for each value sequentially. `nruns` A positive integer with default value equal to 50. It is used to define the number of runs in the NMF function. `rank` A character string or a positive integer, which defines the rank used in the optimization procedure to detect the change points. If rank = "optimal", which is also the default value, then the optimal rank is used. If rank = a positive integer value, say 4, then a predetermined rank is used. `algtype` A character string, which defines the algorithm to be used in the NMF function. By default it is set to "brunet". See the "Algorithms" section of `nmf` for more information on the available algorithms. `changepoints` A vector of positive integers with default value equal to `NULL`. It is used to specify whether change points exist in the input Y, and thus whether Y should be split into multiple stationary segments and networks estimated separately for each segment. If change points, say c(100, 200) are specified, Y is split at the 100th and 200th row to correspond to 3 stationary segments. Each stationary segment is then estimated sequentially, and a list is returned where each component corresponds to a stationary segment.

### Value

A matrix (or more specifically, an adjacency matrix) denoting the network (or clustering) structure between components of Y. If lambda is a vector, a list of adjacency matrices is returned, where each element of the list corresponds to an element in lambda.

### Author(s)

Martin Ondrus, mondrus@ualberta.ca, Ivor Cribben, cribben@ualberta.ca

### References

"Factorized Binary Search: a novel technique for change point detection in multivariate high-dimensional time series networks", Ondrus et al. (2021), <arXiv:2103.06347>.

### Examples

```
## Estimating the network for a multivariate data set, "sim2" with the settings:
## nruns = 10 and lambda = 0.5 where the latter specifies the cutoff based method
est.net(sim2, lambda = 0.5, nruns = 4)

```

fabisearch documentation built on March 18, 2022, 6 p.m.