learnDiagGaussian {MixAll} | R Documentation |
This function learn the optimal mixture model when the class labels are known
according to the criterion
among the list of model given in models
.
learnDiagGaussian(data, labels, prop = NULL, models = clusterDiagGaussianNames(prop = "equal"), algo = "simul", nbIter = 100, epsilon = 1e-08, criterion = "ICL", nbCore = 1) learnPoisson(data, labels, prop = NULL, models = clusterPoissonNames(prop = "equal"), algo = "simul", nbIter = 100, epsilon = 1e-08, criterion = "ICL", nbCore = 1) learnGamma(data, labels, prop = NULL, models = clusterGammaNames(prop = "equal"), algo = "simul", nbIter = 100, epsilon = 1e-08, criterion = "ICL", nbCore = 1) learnCategorical(data, labels, prop = NULL, models = clusterCategoricalNames(prop = "equal"), algo = "simul", nbIter = 100, epsilon = 1e-08, criterion = "ICL", nbCore = 1)
data |
frame or matrix containing the data. Rows correspond to observations and columns correspond to variables. If the data set contains NA values, they will be estimated during the estimation process. |
labels |
vector or factors giving the label class. |
prop |
[ |
models |
[ |
algo |
character defining the algo to used in order to learn the model. Possible values: "simul" (default), "impute" (faster but can produce biased results). |
nbIter |
integer giving the number of iterations to do. algo is "impute" this is the maximal authorized number of iterations. Default is 100. |
epsilon |
real giving the variation of the log-likelihood for stopping the iterations. Not used if algo is "simul". Default value is 1e-08. |
criterion |
character defining the criterion to select the best model. The best model is the one with the lowest criterion value. Possible values: "BIC", "AIC", "ML". Default is "ICL". |
nbCore |
integer defining the number of processors to use (default is 1, 0 for all). |
An instance of a learned mixture model class.
Serge Iovleff
## A quantitative example with the famous iris data set data(iris) ## get data and target x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x); ## add missing values at random indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2); x[indexes] <- NA; ## learn model model <- learnDiagGaussian( data=x, labels= z, prop = c(1/3,1/3,1/3) , models = clusterDiagGaussianNames(prop = "equal") ) ## get summary summary(model) ## use graphics functions ## Not run: plot(model) ## End(Not run) ## print model ## Not run: print(model) ## End(Not run) ## get estimated missing values missingValues(model)