Control of PLNnetwork fit

Source: R/PLNnetwork.R

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

PLNnetwork_param(
  backend = "nlopt",
  trace = 1,
  n_penalties = 30,
  min_ratio = 0.1,
  penalize_diagonal = TRUE,
  penalty_weights = NULL,
  config_optim = list(),
  inception = NULL
)

Arguments

backend

optimization back used, either "nlopt" or "torch". Default is "nlopt"

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 filled with 1) to adapt the amount of shrinkage to each pairs of node. Must be symmetric with positive values.

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

The list of parameters config_optim controls the optimizers. When "nlopt" is chosen the following entries are relevant

  • "algorithm" the optimization method used by NLOPT among LD type, e.g. "CCSAQ", "MMA", "LBFGS". See NLOPT documentation for further details. Default is "CCSAQ".

  • "maxeval" stop when the number of iteration exceeds maxeval. Default is 10000

  • "ftol_rel" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-8

  • "xtol_rel" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-6

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

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

  • "ftol_abs" stop when an optimization step changes the objective function by less than ftol_abs. Default is 0.0 (disabled)

  • "xtol_abs" stop when an optimization step changes every parameters by less than xtol_abs. Default is 0.0 (disabled)

  • "maxtime" stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (disabled)

When "torch" backend is used, with the following entries are relevant:

  • "maxeval" stop when the number of iteration exceeds maxeval. Default is 10000

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

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

  • "ftol_rel" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-8

  • "xtol_rel" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-6