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Zero Inflated Sparse Poisson lognormal model for network inference

Source: R/ZIPLNnetwork.R
ZIPLNnetwork.Rd

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 the model 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.

Value

an R6 object with class ZIPLNnetworkfamily

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.

See also

The classes ZIPLNfit and ZIPLNnetworkfamily

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myZIPLNs <- ZIPLNnetwork(Abundance ~ 1, data = trichoptera, zi = "single")
#> 
#>  Initialization...
#>  Adjusting 30 ZI-PLN with sparse inverse covariance estimation and single specific parameter(s) in Zero inflation component.
#> 	sparsifying penalty = 6.993458 
	sparsifying penalty = 6.459654 
	sparsifying penalty = 5.966594 
	sparsifying penalty = 5.511169 
	sparsifying penalty = 5.090506 
	sparsifying penalty = 4.701952 
	sparsifying penalty = 4.343056 
	sparsifying penalty = 4.011554 
	sparsifying penalty = 3.705356 
	sparsifying penalty = 3.422529 
	sparsifying penalty = 3.16129 
	sparsifying penalty = 2.919992 
	sparsifying penalty = 2.697111 
	sparsifying penalty = 2.491243 
	sparsifying penalty = 2.301089 
	sparsifying penalty = 2.125449 
	sparsifying penalty = 1.963215 
	sparsifying penalty = 1.813364 
	sparsifying penalty = 1.674952 
	sparsifying penalty = 1.547104 
	sparsifying penalty = 1.429015 
	sparsifying penalty = 1.31994 
	sparsifying penalty = 1.21919 
	sparsifying penalty = 1.12613 
	sparsifying penalty = 1.040173 
	sparsifying penalty = 0.9607779 
	sparsifying penalty = 0.8874426 
	sparsifying penalty = 0.8197048 
	sparsifying penalty = 0.7571375 
	sparsifying penalty = 0.6993458 

#>  DONE!