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

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



an object of class "formula": a symbolic description of the model to be fitted.


an optional data frame, list or environment (or object coercible by 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.


an optional vector specifying a subset of observations to be used in the fitting process.


a vector of integer containing the successive number of clusters (or components) to be considered


a list-like structure for controlling the optimization, with default generated by PLNmixture_param(). See the associated documentation for details.


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


## Use future to dispatch the computations on 2 workers
if (FALSE) {
future::plan("multisession", workers = 2)

trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myMixtures <- PLNmixture(Abundance ~ 1 + offset(log(Offset)), clusters = 1:4, data = trichoptera,
                         control = PLNmixture_param(smoothing = 'none'))
#>  Initialization...
#>  Adjusting 4 PLN mixture models.
#> 	number of cluster = 1 
	number of cluster = 2 
	number of cluster = 3 
	number of cluster = 4 

#>  Post-treatments
#>  DONE!

# Shut down parallel workers
if (FALSE) {