Zero Inflated Sparse Poisson lognormal model for network inference

Source: R/ZIPLNnetwork.R

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).

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.

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 = 7.69066 
	sparsifying penalty = 7.103638 
	sparsifying penalty = 6.561423 
	sparsifying penalty = 6.060596 
	sparsifying penalty = 5.597996 
	sparsifying penalty = 5.170705 
	sparsifying penalty = 4.77603 
	sparsifying penalty = 4.41148 
	sparsifying penalty = 4.074755 
	sparsifying penalty = 3.763733 
	sparsifying penalty = 3.47645 
	sparsifying penalty = 3.211096 
	sparsifying penalty = 2.965995 
	sparsifying penalty = 2.739604 
	sparsifying penalty = 2.530492 
	sparsifying penalty = 2.337342 
	sparsifying penalty = 2.158934 
	sparsifying penalty = 1.994145 
	sparsifying penalty = 1.841933 
	sparsifying penalty = 1.70134 
	sparsifying penalty = 1.571478 
	sparsifying penalty = 1.451529 
	sparsifying penalty = 1.340735 
	sparsifying penalty = 1.238398 
	sparsifying penalty = 1.143872 
	sparsifying penalty = 1.056561 
	sparsifying penalty = 0.9759147 
	sparsifying penalty = 0.901424 
	sparsifying penalty = 0.832619 
	sparsifying penalty = 0.769066 

#>  DONE!