PLNnetwork: Poisson lognormal model towards sparse network inference

In PLNmodels: Poisson Lognormal Models

Poisson lognormal model towards sparse network inference

Description

Fit the sparse inverse covariance variant of the Poisson lognormal with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets).

Usage

PLNnetwork(
  formula,
  data,
  subset,
  weights,
  penalties = NULL,
  control = PLNnetwork_param()
)

Arguments

formula	an object of class "formula": a symbolic description of the model to be fitted.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
weights	an optional vector of observation weights to be used in the fitting process.
penalties	an optional vector of positive real number controlling the level of sparsity of the underlying network. if NULL (the default), will be set internally. See PLNnetwork_param() for additional tuning of the penalty.
control	a list-like structure for controlling the optimization, with default generated by PLNnetwork_param(). See the corresponding documentation for details;

Value

an R6 object with class PLNnetworkfamily, which contains a collection of models with class PLNnetworkfit

See Also

The classes PLNnetworkfamily and PLNnetworkfit, and the and the configuration function PLNnetwork_param().

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)

PLNmodels documentation built on Aug. 24, 2023, 5:11 p.m.