ZIPLNnetwork: Zero Inflated Sparse Poisson lognormal model for network...
In PLN-team/PLNmodels: Poisson Lognormal Models

ZIPLNnetwork

R Documentation

Zero Inflated 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

ZIPLNnetwork(
  formula,
  data,
  subset,
  weights,
  zi = c("single", "row", "col"),
  penalties = NULL,
  control = ZIPLNnetwork_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.
`zi`	a character describing the model used for zero inflation, either of "single" (default, one parameter shared by all counts) "col" (one parameter per variable / feature) "row" (one parameter per sample / individual). If covariates are specified in the formula RHS (see details) this parameter is ignored.
`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 `ZIPLNnetwork_param()`. See the associated documentation for details.

Details

Covariates for the Zero-Inflation parameter (using a logistic regression model) can be specified in the formula RHS using the pipe (⁠~ PLN effect | ZI effect⁠) to separate covariates for the PLN part of the model from those for the Zero-Inflation part. Note that different covariates can be used for each part.

Value

an R6 object with class ZIPLNnetworkfamily

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myZIPLNs <- ZIPLNnetwork(Abundance ~ 1, data = trichoptera, zi = "single")

PLN-team/PLNmodels documentation built on April 15, 2024, 9:01 a.m.