binary_segmentation {changepointsHD} | R Documentation |
Estimates multiple change-points using the binary-segmentation method. This does a breadth first search and uses the specified single change-point method for each sub-search.
binary_segmentation(object, method, thresh = 0, buff = 100, method_params = list()) ## S4 method for signature 'changepointsMod' binary_segmentation(object, method, thresh = 0, buff = 100, method_params = list())
object |
Corresponding |
method |
changepointHD method for finding single change-point. |
thresh |
Stopping threshold for cost comparison. |
buff |
Distance from edge of sample to be maintained during search. |
method_params |
List of additional parameters for |
An updated version of the change-point model. The update will effect:
1) An estimate for the current set of change-points. 2) The mod_list
,
this will correspond to all the active single change-point models
generated during the binary-segmentation procedure. Acitve models
correspond to models that have not been superseded by more granular
models. 3) The mod_range
, this corresponds to the range of
observations covered by each model. It can be used to determine which
models are active.
Leland Bybee <lelandb@umich.edu>
set.seed(334) mcp_data = read.table(system.file("extdata", "mcp.txt", package="changepointsHD")) mcp_data = as.matrix(mcp_data) # prox gradient black-box method cov_est = cov(mcp_data) init = solve(cov_est) res_map = prox_gradient_mapping(mcp_data, init, 0.1, 0.99, 0.1, 100, 1e-20) # prox gradient black-box ll res_ll = prox_gradient_ll(mcp_data, res_map, 0.1) prox_gradient_params=list() prox_gradient_params$update_w = 0.1 prox_gradient_params$update_change = 0.99 prox_gradient_params$regularizer = 0.1 prox_gradient_params$max_iter = 1 prox_gradient_params$tol = 1e-5 prox_gradient_ll_params=list() prox_gradient_ll_params$regularizer = 0.1 simulated_annealing_params = list() simulated_annealing_params$buff=10 changepoints_mod = changepointsMod(bbmod=prox_gradient_mapping, log_likelihood=prox_gradient_ll, bbmod_params=prox_gradient_params, ll_params=prox_gradient_ll_params, part_values=list(init, init), data=list(mcp_data)) changepoints_mod = binary_segmentation(changepoints_mod, method=simulated_annealing, thresh=0, buff=10, method_params=simulated_annealing_params)