The function PLNmixture produces a collection of models which are instances of object with class PLNmixturefit. A PLNmixturefit (say, with k components) is itself a collection of k PLNfit.

This class comes with a set of methods, some of them being useful for the user: See the documentation for ...

The function PLNmixture, the class PLNmixturefamily

## Active bindings

n

number of samples

p

number of dimensions of the latent space

k

number of components

d

number of covariates

components

components of the mixture (PLNfits)

latent

a matrix: values of the latent vector (Z in the model)

latent_pos

a matrix: values of the latent position vector (Z) without covariates effects or offset

posteriorProb

matrix ofposterior probability for cluster belonging

memberships

vector for cluster index

mixtureParam

vector of cluster proportions

optim_par

a list with parameters useful for monitoring the optimization

nb_param

number of parameters in the current PLN model

entropy_clustering

Entropy of the variational distribution of the cluster (multinomial)

entropy_latent

Entropy of the variational distribution of the latent vector (Gaussian)

entropy

Full entropy of the variational distribution (latent vector + clustering)

loglik

variational lower bound of the loglikelihood

loglik_vec

element-wise variational lower bound of the loglikelihood

BIC

variational lower bound of the BIC

ICL

variational lower bound of the ICL (include entropy of both the clustering and latent distributions)

R_squared

approximated goodness-of-fit criterion

criteria

a vector with loglik, BIC, ICL, and number of parameters

model_par

a list with the matrices of parameters found in the model (Theta, Sigma, Mu and Pi)

vcov_model

character: the model used for the covariance (either "spherical", "diagonal" or "full")

fitted

a matrix: fitted values of the observations (A in the model)

group_means

a matrix of group mean vectors in the latent space.

## Methods

### Method new()

Optimize a the

Initialize a PLNmixturefit model

#### Arguments

responses

the matrix of responses common to every models

covariates

the matrix of covariates common to every models

offsets

the matrix of offsets common to every models

config

a list for controlling the optimization

### Method predict()

Predict group of new samples

#### Usage

PLNmixturefit$predict( newdata, type = c("posterior", "response", "position"), prior = matrix(rep(1/self$k, self$k), nrow(newdata), self$k, byrow = TRUE),
control = PLNmixture_param(),
envir = parent.frame()
)

#### Arguments

newdata

A data frame in which to look for variables, offsets and counts with which to predict.

type

The type of prediction required. The default posterior are posterior probabilities for each group , response is the group with maximal posterior probability and latent is the averaged latent coordinate (without offset and nor covariate effects), with weights equal to the posterior probabilities.

prior

User-specified prior group probabilities in the new data. The default uses a uniform prior.

control

a list-like structure for controlling the fit. See PLNmixture_param() for details.

envir

Environment in which the prediction is evaluated

### Method plot_clustering_data()

Plot the matrix of expected mean counts (without offsets, without covariate effects) reordered according the inferred clustering

PLNmixturefit$plot_clustering_data( main = "Expected counts reorder by clustering", plot = TRUE, log_scale = TRUE ) #### Arguments main character. A title for the plot. An hopefully appropriate title will be used by default. plot logical. Should the plot be displayed or sent back as ggplot object log_scale logical. Should the color scale values be log-transform before plotting? Default is TRUE. #### Returns a ggplot graphic ### Method plot_clustering_pca() Plot the individual map of a PCA performed on the latent coordinates, where individuals are colored according to the memberships #### Usage PLNmixturefit$plot_clustering_pca(
main = "Clustering labels in Individual Factor Map",
plot = TRUE
)

#### Arguments

main

character. A title for the plot. An hopefully appropriate title will be used by default.

plot

logical. Should the plot be displayed or sent back as ggplot object

#### Returns

a ggplot graphic

### Method postTreatment()

Update fields after optimization

### Method print()

User friendly print method

#### Arguments

deep

Whether to make a deep clone.