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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 = 8.17123 
	sparsifying penalty = 7.547527 
	sparsifying penalty = 6.97143 
	sparsifying penalty = 6.439307 
	sparsifying penalty = 5.9478 
	sparsifying penalty = 5.49381 
	sparsifying penalty = 5.074472 
	sparsifying penalty = 4.687142 
	sparsifying penalty = 4.329376 
	sparsifying penalty = 3.998919 
	sparsifying penalty = 3.693685 
	sparsifying penalty = 3.411749 
	sparsifying penalty = 3.151333 
	sparsifying penalty = 2.910795 
	sparsifying penalty = 2.688616 
	sparsifying penalty = 2.483396 
	sparsifying penalty = 2.293841 
	sparsifying penalty = 2.118754 
	sparsifying penalty = 1.957031 
	sparsifying penalty = 1.807653 
	sparsifying penalty = 1.669676 
	sparsifying penalty = 1.542231 
	sparsifying penalty = 1.424514 
	sparsifying penalty = 1.315782 
	sparsifying penalty = 1.21535 
	sparsifying penalty = 1.122583 
	sparsifying penalty = 1.036897 
	sparsifying penalty = 0.9577517 
	sparsifying penalty = 0.8846473 
	sparsifying penalty = 0.817123 

#>  DONE!