PLNnetwork: Sparse Poisson lognormal model for network inference

In PLN-team/PLNmodels: Poisson Lognormal Models

Sparse Poisson lognormal model for network inference

Description

Perform sparse inverse covariance estimation for the Zero Inflated Poisson lognormal model using a variational algorithm. Iterate over a range of logarithmically spaced sparsity parameter values. Use the (g)lm syntax to specify the model (including covariates and 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)

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