est.net: Sparse network estimation using non-negative matrix...

View source: R/est.net.R

est.netR 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.