SegmentLoss: SegmentLoss
In lorenzha/hdcd: High-Dimensional Changepoint Detection

Description Usage Arguments Details Value

This closure returns a function which calculates the loss for the given segment of the data.

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SegmentLoss(n_obs, lambda, penalize_diagonal = FALSE, standardize = TRUE,
  threshold = 1e-07, method = c("nodewise_regression", "summed_regression",
  "ratio_regression", "glasso"), ...)

`n_obs`	Total number of observations
`lambda`	Positive numeric value. This is the regularization parameter in the single Lasso fits. This value is ignored if FUN is not NULL.
`penalize_diagonal`	Boolean, should the diagonal elements of the precision matrix be penalized by λ? This value is ignored if FUN is not NULL.
`standardize`	Boolean. If TRUE the penalty parameter λ will be adjusted for every dimension in the single Lasso fits according to the standard deviation in the data.
`threshold`	The threshold for halting the iteration in `glasso` or `glmnet`. In the former it controls the absolute change of single parameters in the latter it controls the total objective value. This value is ignored if FUN is not NULL.
`method`	Which estimator should be used? Possible choices are nodewise_regression: Nodewise regression is based on a single node that needs to be specified with an additional parameter `node` pointing to the column index of the node of interest. Uses `glmnet` internally. See Kovács (2016) for details. summed_regression: Summed nodewise regression sums up the residual variances of nodewise regression over all nodes. Uses `glasso` internally. See Kovács (2016) for details. ratio_regression: Likelihood ratio based regression sums the pseudo-profile-likelihood over all nodes. Uses `glasso` internally. See Kovács (2016) for details. glasso: The graphical Lasso uses the approach of Friedman et al (2007). In contrast to the other approaches the exact likelihood the whole graphical model is computed and used as loss. This value is ignored if `FUN` is not `NULL`.
`...`	Further arguments supplied to the select method.

Depending on the desired method and the tuning parameters a different loss function will be parametrized and returned. If method nodewise_regression is selected, the additional argument node must be supplied to determine on which node (dimension) it should be performed.

A parametrized loss function

lorenzha/hdcd documentation built on Sept. 2, 2018, 8:20 p.m.