PLNPCA | R Documentation |

Fit the PCA variants of the Poisson lognormal with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets).

```
PLNPCA(formula, data, subset, weights, ranks = 1:5, control = PLNPCA_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. |

`ranks` |
a vector of integer containing the successive ranks (or number of axes to be considered) |

`control` |
a list-like structure for controlling the optimization, with default generated by |

an R6 object with class `PLNPCAfamily`

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

The classes `PLNPCAfamily`

and `PLNPCAfit`

, and the configuration function `PLNPCA_param()`

.

```
#' ## Use future to dispatch the computations on 2 workers
## Not run:
future::plan("multisession", workers = 2)
## End(Not run)
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCA <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
# Shut down parallel workers
## Not run:
future::plan("sequential")
## End(Not run)
```

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