PLNmixturefit {PLNmodels}

An R6 Class to represent a PLNfit in a mixture framework

Description

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 ...

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

Public methods

• PLNmixturefit$new()

• PLNmixturefit$optimize()

• PLNmixturefit$predict()

• PLNmixturefit$plot_clustering_data()

• PLNmixturefit$plot_clustering_pca()

• PLNmixturefit$postTreatment()

• PLNmixturefit$show()

• PLNmixturefit$print()

• PLNmixturefit$clone()

Method new()

Optimize a the

Initialize a PLNmixturefit model

Usage

PLNmixturefit$new(
  responses,
  covariates,
  offsets,
  posteriorProb,
  formula,
  control
)

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

posteriorProb

matrix ofposterior probability for cluster belonging

formula

model formula used for fitting, extracted from the formula in the upper-level call

control

a list for controlling the optimization.

Method optimize()

Optimize a PLNmixturefit model

Usage

PLNmixturefit$optimize(responses, covariates, offsets, config)

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

Usage

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

Usage

PLNmixturefit$postTreatment(
  responses,
  covariates,
  offsets,
  weights,
  config_post,
  config_optim,
  nullModel
)

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

weights

an optional vector of observation weights to be used in the fitting process.

config_post

a list for controlling the post-treatment

config_optim

a list for controlling the optimization during the post-treatment computations

nullModel

null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable.

Method show()

User friendly print method

Usage

PLNmixturefit$show()

Method print()

User friendly print method

Usage

PLNmixturefit$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

PLNmixturefit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

The function PLNmixture, the class PLNmixturefamily