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

[Up] [Top]

Documentation for package ‘EMMIXSSL’ version 1.1.1

Help Pages

Classifier_Bayes Classifier based on Bayes rule
cov2vec Transform a variance matrix into a vector
discriminant_beta Discriminant function
EMMIXSSL Fitting Gaussian mixture models
errorrate Error rate of the Bayes rule for two-class Gaussian homoscedastic model
gastrodata Gastrointestinal dataset
gastro_label_binary Gastrointestinal binary labels
gastro_label_trinary Gastrointestinal trinary labels
get_clusterprobs Posterior probability
get_entropy Shannon entropy
initialvalue Initial values for ECM
list2par Transfer a list into a vector
loglk_full Full log-likelihood function
loglk_ig Log likelihood for partially classified data with ingoring the missing mechanism
loglk_miss Log likelihood function formed on the basis of the missing-label indicator
logsumexp log summation of exponential function
makelabelmatrix Label matrix
neg_objective_function Negative objective function for EMMIXSSL
normalise_logprob Normalize log-probability
par2list Transfer a vector into a list
pro2vec Transfer a probability vector into a vector
rlabel Generation of a missing-data indicator
rmix Normal mixture model generator.
vec2cov Transform a vector into a matrix
vec2pro Transfer an informative vector to a probability vector