PLNnetwork_param | R Documentation |

Helper to define list of parameters to control the PLN fit. All arguments have defaults.

```
PLNnetwork_param(
backend = c("nlopt", "torch"),
inception_cov = c("full", "spherical", "diagonal"),
trace = 1,
n_penalties = 30,
min_ratio = 0.1,
penalize_diagonal = TRUE,
penalty_weights = NULL,
config_post = list(),
config_optim = list(),
inception = NULL
)
```

`backend` |
optimization back used, either "nlopt" or "torch". Default is "nlopt" |

`inception_cov` |
Covariance structure used for the inception model used to initialize the PLNfamily. Defaults to "full" and can be constrained to "diagonal" and "spherical". |

`trace` |
a integer for verbosity. |

`n_penalties` |
an integer that specifies the number of values for the penalty grid when internally generated. Ignored when penalties is non |

`min_ratio` |
the penalty grid ranges from the minimal value that produces a sparse to this value multiplied by |

`penalize_diagonal` |
boolean: should the diagonal terms be penalized in the graphical-Lasso? Default is |

`penalty_weights` |
either a single or a list of p x p matrix of weights (default: all weights equal to 1) to adapt the amount of shrinkage to each pairs of node. Must be symmetric with positive values. |

`config_post` |
a list for controlling the post-treatment (optional bootstrap, jackknife, R2, etc). |

`config_optim` |
a list for controlling the optimizer (either "nlopt" or "torch" backend). See details |

`inception` |
Set up the parameters initialization: by default, the model is initialized with a multivariate linear model applied on log-transformed data, and with the same formula as the one provided by the user. However, the user can provide a PLNfit (typically obtained from a previous fit), which sometimes speeds up the inference. |

See `PLN_param()`

for a full description of the generic optimization parameters. PLNnetwork_param() also has two additional parameters controlling the optimization due the inner-outer loop structure of the optimizer:

"ftol_out" outer solver stops when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6

"maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 50

list of parameters configuring the fit.

`PLN_param()`

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