sum_logL_GP_clust: Compute a mixture of Gaussian log-likelihoods

View source: R/likelihoods.R

sum_logL_GP_clustR Documentation

Compute a mixture of Gaussian log-likelihoods

Description

During the prediction step of MagmaClust, an EM algorithm is used to compute the maximum likelihood estimator of the hyper-parameters along with mixture probabilities for the new individual/task. This function implements the quantity that is maximised (i.e. a sum of Gaussian log-likelihoods, weighted by their mixture probabilities). It can also be used to monitor the EM algorithm when providing the 'prop_mixture' argument, for proper penalisation of the full log-likelihood.

Usage

sum_logL_GP_clust(
  hp,
  db,
  mixture,
  mean,
  kern,
  post_cov,
  prop_mixture = NULL,
  pen_diag
)

Arguments

hp

A tibble, data frame or named vector of hyper-parameters.

db

A tibble containing data we want to evaluate the logL on. Required columns: Input, Output. Additional covariate columns are allowed.

mixture

A tibble or data frame, indicating the mixture probabilities of each cluster for the new individual/task.

mean

A list of hyper-posterior mean parameters for all clusters.

kern

A kernel function.

post_cov

A list of hyper-posterior covariance parameters for all clusters.

prop_mixture

A tibble or a named vector. Each name of column or element should refer to a cluster. The value associated with each cluster is a number between 0 and 1, corresponding to the mixture proportions.

pen_diag

A jitter term that is added to the covariance matrix to avoid numerical issues when inverting, in cases of nearly singular matrices.

Value

A number, expectation of mixture of Gaussian log-likelihoods in the prediction step of MagmaClust. This quantity is supposed to increase at each step of the EM algorithm, and can be used for monitoring the procedure.

Examples

TRUE

MagmaClustR documentation built on June 29, 2024, 1:06 a.m.