est.net {fabisearch} | 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
|
changepoints |
A vector of positive integers with default value equal to |
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)