PLN_param {PLNmodels}R Documentation

Control of a PLN fit

Description

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

Usage

PLN_param(
  backend = c("nlopt", "torch"),
  trace = 1,
  covariance = c("full", "diagonal", "spherical", "fixed"),
  Omega = NULL,
  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", "spherical" or "fixed". Default is "full".

Omega

precision matrix of the latent variables. Inverse of Sigma. Must be specified if covariance is "fixed"

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

When "torch" backend is used (only for PLN and PLNLDA for now), the following entries are relevant:

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):

Value

list of parameters configuring the fit.


[Package PLNmodels version 1.2.0 Index]