Control of PLNnetwork fit

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

PLNnetwork_param(
  backend = c("nlopt", "torch"),
  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 back used, either "nlopt" or "torch". Default is "nlopt"

inception_cov

Covariance structure used for the inception model used to initialize the PLNfamily. Defaults to "full" and can be constrained to "diagonal" and "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-treatment (optional bootstrap, jackknife, R2, etc).

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 PLN_param() for a full description of the generic optimization parameters. PLNnetwork_param() also has two additional parameters controlling the optimization due the inner-outer loop structure of the optimizer:

• "ftol_out" outer solver stops when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6

• "maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 50

See also

PLN_param()