PLNnetwork | 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).

```
PLNnetwork(
formula,
data,
subset,
weights,
penalties = NULL,
control = PLNnetwork_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. |

`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 |

an R6 object with class `PLNnetworkfamily`

, which contains
a collection of models with class `PLNnetworkfit`

The classes `PLNnetworkfamily`

and `PLNnetworkfit`

, and the and the configuration function `PLNnetwork_param()`

.

```
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
```

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