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 = 7.932663 
	sparsifying penalty = 7.327169 
	sparsifying penalty = 6.767893 
	sparsifying penalty = 6.251305 
	sparsifying penalty = 5.774149 
	sparsifying penalty = 5.333413 
	sparsifying penalty = 4.926318 
	sparsifying penalty = 4.550296 
	sparsifying penalty = 4.202976 
	sparsifying penalty = 3.882167 
	sparsifying penalty = 3.585844 
	sparsifying penalty = 3.31214 
	sparsifying penalty = 3.059327 
	sparsifying penalty = 2.825811 
	sparsifying penalty = 2.610119 
	sparsifying penalty = 2.410891 
	sparsifying penalty = 2.22687 
	sparsifying penalty = 2.056895 
	sparsifying penalty = 1.899894 
	sparsifying penalty = 1.754877 
	sparsifying penalty = 1.620928 
	sparsifying penalty = 1.497204 
	sparsifying penalty = 1.382924 
	sparsifying penalty = 1.277367 
	sparsifying penalty = 1.179866 
	sparsifying penalty = 1.089808 
	sparsifying penalty = 1.006624 
	sparsifying penalty = 0.9297892 
	sparsifying penalty = 0.8588192 
	sparsifying penalty = 0.7932663 

#>  DONE!