ZIPLNnetwork  R Documentation 
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()
)
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

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 
control 
a listlike structure for controlling the optimization, with default generated by 
Covariates for the ZeroInflation 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 ZeroInflation part.
Note that different covariates can be used for each part.
an R6 object with class ZIPLNnetworkfamily
The classes ZIPLNfit
and ZIPLNnetworkfamily
data(trichoptera)
trichoptera < prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myZIPLNs < ZIPLNnetwork(Abundance ~ 1, data = trichoptera, zi = "single")
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