ZIPLN_param | R Documentation |

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

```
ZIPLN_param(
backend = c("nlopt"),
trace = 1,
covariance = c("full", "diagonal", "spherical", "fixed", "sparse"),
Omega = NULL,
penalty = 0,
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" |

`trace` |
a integer for verbosity. |

`covariance` |
character setting the model for the covariance matrix. Either "full", "diagonal", "spherical" or "fixed". Default is "full". |

`Omega` |
precision matrix of the latent variables. Inverse of Sigma. Must be specified if |

`penalty` |
a user-defined penalty to sparsify the residual covariance. Defaults to 0 (no sparsity). |

`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-treatments (optional bootstrap, jackknife, R2, etc.). See details |

`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()`

and `PLNnetwork_param()`

for a full description of the generic optimization parameters. Like `PLNnetwork_param()`

, ZIPLN_param() has two 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_out`

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 100 and one additional parameter controlling the form of the variational approximation of the zero inflation:"approx_ZI" either uses an exact or approximated conditional distribution for the zero inflation. Default is FALSE

list of parameters used during the fit and post-processing steps

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