R: Poisson lognormal mixture model

PLNmixture {PLNmodels}

R Documentation

Poisson lognormal mixture model

Description

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

Usage

PLNmixture(formula, data, subset, clusters = 1:5, control = PLNmixture_param())

Arguments

`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.
`clusters`	a vector of integer containing the successive number of clusters (or components) to be considered
`control`	a list-like structure for controlling the optimization, with default generated by `PLNmixture_param()`. See the associated documentation for details.

Value

an R6 object with class PLNmixturefamily, which contains a collection of models with class PLNmixturefit

Examples

## 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)
myMixtures <- PLNmixture(Abundance ~ 1 + offset(log(Offset)), clusters = 1:4, data = trichoptera,
                         control = PLNmixture_param(smoothing = 'none'))

# Shut down parallel workers
## Not run: 
future::plan("sequential")

## End(Not run)