Wrapper around the function flsa::flsa, which computes the
fused lasso signal approximator (see reference). Like agraph, this function
takes a signal on graph and returns a clustering thereof into a piecewise-constant
signal. The difference with agraph is the estimation method: agraph works well when the
true signal is sparse and its computation time scales well to large graphs.
flsa_graph(gamma, graph, lambda)A list with the following elements:
result: matrix whose rows are the segmented output of input signal gamma, for each value of lambda
bic, gcv, and aic: vectors of length length(lambda), giving the BIC, GCV, and AIC criteria for each value of lambda. See references below.
model_dim, nll: vectors of length length(lambda), giving the model dimension and negative log-likelihood for each value of lambda. See reference below for the definition of these terms.
entry vector to regularize
graph (an igraph object) giving the regularization structure
regularizing constant
Hoefling, H., A Path Algorithm for the Fused Lasso Signal Approximator, Journal of Computational and Graphical Statistics (2010) tools:::Rd_expr_doi("10.1198/jcgs.2010.09208")
graphseg::agraph()