PLNnetwork {PLNmodels} | R Documentation |
Sparse Poisson lognormal model for network inference
Description
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
PLNnetwork(
formula,
data,
subset,
weights,
penalties = NULL,
control = PLNnetwork_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. |
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 list-like structure for controlling the optimization, with default generated by |
Value
an R6 object with class PLNnetworkfamily
, which contains
a collection of models with class PLNnetworkfit
See Also
The classes PLNnetworkfamily
and PLNnetworkfit
, and the and the configuration function PLNnetwork_param()
.
Examples
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)