PLNLDA_param: Control of a PLNLDA fit

In PLN-team/PLNmodels: Poisson Lognormal Models

Control of a PLNLDA fit

Description

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

Usage

PLNLDA_param(
  backend = c("nlopt", "torch"),
  trace = 1,
  covariance = c("full", "diagonal", "spherical"),
  config_post = list(),
  config_optim = list(),
  inception = NULL
)

Arguments

backend	optimization back used, either "nlopt" or "torch". Default is "nlopt"
trace	a integer for verbosity.
covariance	character setting the model for the covariance matrix. Either "full", "diagonal" or "spherical". Default is "full".
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.

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

Value

list of parameters configuring the fit.

PLN-team/PLNmodels documentation built on Oct. 13, 2024, 4:01 a.m.