Control of PLNPCA fit

Source: R/PLNPCA.R

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

PLNPCA_param(
  backend = "nlopt",
  trace = 1,
  config_optim = list(),
  config_post = list(),
  inception = NULL
)

Arguments

backend

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

trace

a integer for verbosity.

config_optim

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

config_post

a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). 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_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 (only for PLN and PLNLDA for now), the following entries are relevant:

  • "algorithm" the optimizer used by torch among RPROP (default), RMSPROP, ADAM and ADAGRAD

  • "maxeval" stop when the number of iteration exceeds maxeval. Default is 10 000

  • "numepoch" stop training once this number of epochs exceeds numepoch. Set to -1 to enable infinite training. Default is 1 000

  • "num_batch" number of batches to use during training. Defaults to 1 (use full dataset at each epoch)

  • "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

  • "lr" learning rate. Default is 0.1.

  • "momentum" momentum factor. Default is 0 (no momentum). Only used in RMSPROP

  • "weight_decay" Weight decay penalty. Default is 0 (no decay). Not used in RPROP

  • "step_sizes" pair of minimal (default: 1e-6) and maximal (default: 50) allowed step sizes. Only used in RPROP

  • "etas" pair of multiplicative increase and decrease factors. Default is (0.5, 1.2). Only used in RPROP

  • "centered" if TRUE, compute the centered RMSProp where the gradient is normalized by an estimation of its variance weight_decay (L2 penalty). Default to FALSE. Only used in RMSPROP

The list of parameters config_post controls the post-treatment processing (for most PLN*() functions), with the following entries (defaults may vary depending on the specific function, check config_post_default_* for defaults values):

  • jackknife boolean indicating whether jackknife should be performed to evaluate bias and variance of the model parameters. Default is FALSE.

  • bootstrap integer indicating the number of bootstrap resamples generated to evaluate the variance of the model parameters. Default is 0 (inactivated).

  • variational_var boolean indicating whether variational Fisher information matrix should be computed to estimate the variance of the model parameters (highly underestimated). Default is FALSE.

  • sandwich_var boolean indicating whether sandwich estimation should be used to estimate the variance of the model parameters (highly underestimated). Default is FALSE.

  • rsquared boolean indicating whether approximation of R2 based on deviance should be computed. Default is TRUE