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Control of ZIPLNnetwork fit

Source: R/ZIPLNnetwork.R
ZIPLNnetwork_param.Rd

Helper to define list of parameters to control the ZIPLNnetwork fit. All arguments have defaults.

Usage

ZIPLNnetwork_param(
  backend = c("builtin", "nlopt"),
  inception_cov = c("full", "spherical", "diagonal"),
  trace = 1,
  n_penalties = 30,
  min_ratio = 0.1,
  penalize_diagonal = TRUE,
  penalty_weights = NULL,
  config_post = list(),
  config_optim = list(),
  inception = NULL
)

Arguments

backend

optimization backend, either "builtin" (default, joint Newton on (M,ψ,R), combined with the partial E-step maxit_ve = 1) or "nlopt" (CCSAQ). "builtin" consistently finds a better ELBO across the penalty path at the cost of being slower.

inception_cov

Covariance structure used for the inception PLN: "full" (default), "diagonal" or "spherical". Non-full structures are now fully supported: when inception_cov != "full", the penalty grid is built from the empirical covariance of latent residuals M − X·B (a full-rank proxy for Σ), avoiding the broken max_pen = 0 that previously occurred with diagonal/spherical.

trace

a integer for verbosity.

n_penalties

an integer that specifies the number of values for the penalty grid when internally generated. Ignored when penalties is non NULL

min_ratio

the penalty grid ranges from the minimal value that produces a sparse to this value multiplied by min_ratio. Default is 0.1.

penalize_diagonal

boolean: should the diagonal terms be penalized in the graphical-Lasso? Default is TRUE

penalty_weights

either a single or a list of p x p matrix of weights (default: all weights equal to 1) to adapt the amount of shrinkage to each pairs of node. Must be symmetric with positive values.

config_post

a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details

config_optim

a list for controlling the optimizer (either "nlopt" or "torch" backend). See details

inception

Set up the parameters initialization: by default, the model is initialized with a multivariate linear model applied on log-transformed data, and with the same formula as the one provided by the user. However, the user can provide a PLNfit (typically obtained from a previous fit), which sometimes speeds up the inference.

Value

list of parameters configuring the fit.

Details

See PLNnetwork_param() for a full description of the optimization parameters. Note that some defaults values are different than those used in PLNnetwork_param():

  • "ftol_out" (outer loop convergence tolerance the objective function) is set by default to 1e-6

  • "maxit_out" (max number of iterations for the outer loop) is set by default to 50

See also

PLNnetwork_param() and PLN_param()