Package: gmmsslm 1.1.5

gmmsslm: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.

Authors:Ziyang Lyu [aut, cre], Daniel Ahfock [aut], Ryan Thompson [aut], Geoffrey J. McLachlan [aut]

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gmmsslm.pdf |gmmsslm.html
gmmsslm/json (API)

# Install 'gmmsslm' in R:
install.packages('gmmsslm', repos = c('https://lyu9118.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 236 downloads 26 exports 1 dependencies

Last updated 1 years agofrom:41a58bb54a. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 15 2024
R-4.5-winOKOct 15 2024
R-4.5-linuxOKOct 15 2024
R-4.4-winOKOct 15 2024
R-4.4-macOKOct 15 2024
R-4.3-winOKOct 15 2024
R-4.3-macOKOct 15 2024

Exports:bayesclassifiercov2vecdiscriminant_betaerateget_clusterprobsget_entropygmmsslminitialvaluelist2parloglk_fullloglk_igloglk_misslogsumexpmakelabelmatrixneg_objective_functionnormalise_logprobpar2listparaextractplot_missingnesspredictpro2vecrlabelrmixsummaryvec2covvec2pro

Dependencies:mvtnorm

Readme and manuals

Help Manual

Help pageTopics
Bayes' rule of allocationbayesclassifier
Bootstrap Analysis for gmmsslmbootstrap_gmmsslm
Transform a variance matrix into a vectorcov2vec
Discriminant functiondiscriminant_beta
Error rate of the Bayes rule for a g-class Gaussian mixture modelerate
Error rate of the Bayes rule for two-class Gaussian homoscedastic modelerrorrate
Gastrointestinal datasetgastro_data
Posterior probabilityget_clusterprobs
Shannon entropyget_entropy
Fitting Gaussian mixture model to a complete classified dataset or an incomplete classified dataset with/without the missing-data mechanism.gmmsslm
gmmsslmFit ClassgmmsslmFit-class
Initial values for ECMinitialvalue
Transfer a list into a vectorlist2par
Full log-likelihood functionloglk_full
Log likelihood for partially classified data with ingoring the missing mechanismloglk_ig
Log likelihood function formed on the basis of the missing-label indicatorloglk_miss
log summation of exponential functionlogsumexp
Label matrixmakelabelmatrix
Negative objective function for gmmsslneg_objective_function
Normalize log-probabilitynormalise_logprob
Transfer a vector into a listpar2list
Extract parameter list from gmmsslmFit objectsparaextract paraextract,gmmsslmFit-method
Plot Missingness Mechanism and Boxplotplot_missingness
Predict unclassified labelpredict predict,gmmsslmFit-method
Transfer a probability vector into a vectorpro2vec
Generation of a missing-data indicatorrlabel
Normal mixture model generator.rmix
Summary method for gmmsslmFit objectssummary summary,gmmsslmFit-method
Transform a vector into a matrixvec2cov
Transfer an informative vector to a probability vectorvec2pro