PLN_param | R Documentation |

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

```
PLN_param(
backend = c("nlopt", "torch"),
trace = 1,
covariance = c("full", "diagonal", "spherical", "fixed"),
Omega = 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 |

`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. |

The list of parameters `config_optim`

controls the optimizers. When "nlopt" is chosen the following entries are relevant

"algorithm" the optimization method used by NLOPT among LD type, e.g. "CCSAQ", "MMA", "LBFGS". See NLOPT documentation for further details. Default is "CCSAQ".

"maxeval" stop when the number of iteration exceeds maxeval. Default is 10000

"ftol_rel" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-8

"xtol_rel" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-6

"ftol_abs" stop when an optimization step changes the objective function by less than ftol_abs. Default is 0.0 (disabled)

"xtol_abs" stop when an optimization step changes every parameters by less than xtol_abs. Default is 0.0 (disabled)

"maxtime" stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (disabled)

When "torch" backend is used (only for PLN and PLNLDA for now), the following entries are relevant:

"algorithm" the optimizer used by torch among RPROP (default), RMSPROP, ADAM and ADAGRAD

"maxeval" stop when the number of iteration exceeds maxeval. Default is 10 000

"numepoch" stop training once this number of epochs exceeds numepoch. Set to -1 to enable infinite training. Default is 1 000

"num_batch" number of batches to use during training. Defaults to 1 (use full dataset at each epoch)

"ftol_rel" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-8

"xtol_rel" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-6

"lr" learning rate. Default is 0.1.

"momentum" momentum factor. Default is 0 (no momentum). Only used in RMSPROP

"weight_decay" Weight decay penalty. Default is 0 (no decay). Not used in RPROP

"step_sizes" pair of minimal (default: 1e-6) and maximal (default: 50) allowed step sizes. Only used in RPROP

"etas" pair of multiplicative increase and decrease factors. Default is (0.5, 1.2). Only used in RPROP

"centered" if TRUE, compute the centered RMSProp where the gradient is normalized by an estimation of its variance weight_decay (L2 penalty). Default to FALSE. Only used in RMSPROP

The list of parameters `config_post`

controls the post-treatment processing (for most `PLN*()`

functions), with the following entries (defaults may vary depending on the specific function, check `config_post_default_*`

for defaults values):

jackknife boolean indicating whether jackknife should be performed to evaluate bias and variance of the model parameters. Default is FALSE.

bootstrap integer indicating the number of bootstrap resamples generated to evaluate the variance of the model parameters. Default is 0 (inactivated).

variational_var boolean indicating whether variational Fisher information matrix should be computed to estimate the variance of the model parameters (highly underestimated). Default is FALSE.

sandwich_var boolean indicating whether sandwich estimation should be used to estimate the variance of the model parameters (highly underestimated). Default is FALSE.

rsquared boolean indicating whether approximation of R2 based on deviance should be computed. Default is TRUE

list of parameters configuring the fit.

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