Idtmclust-methods: Methods for function Idtmclust in Package `MAINT.Data'

Description

Performs Gaussian model based clustering for interval data

Usage

Idtmclust(Idt, G = 1:9, CovCase=1:4, SelCrit=c("BIC","AIC"),
  Mxt=c("Hom","Het","HomandHet"), control=EMControl())

Value

An object of class IdtMclust providing the optimal (according to BIC) mixture model estimation.

Arguments

Idt: An IData object representing interval-valued entities.
G: An integer vector specifying the numbers of mixture components (clusters) for which the BIC is to be calculated.
CovCase: Configuration of the variance-covariance matrix: a set of integers between 1 and 4.
SelCrit: The model selection criterion.
control: A list of control parameters for EM. The defaults are set by the call EMControl().
Mxt: The type of Gaussian mixture assumed by Idtmclust. Alternatives are “Hom” (default) for homoscedastic mixtures, “Het” for heteroscedastic mixtures, and “HomandHet” for both homoscedastic and heteroscedastic mixtures.

References

Brito, P., Duarte Silva, A. P. (2012), Modelling Interval Data with Normal and Skew-Normal Distributions. Journal of Applied Statistics 39(1), 3--20.

Brito, P., Duarte Silva, A. P. and Dias, J. G. (2015), Probabilistic Clustering of Interval Data. Intelligent Data Analysis 19(2), 293--313.

Fraley, C., Raftery, A. E., Murphy, T. B. and Scrucca, L. (2012), mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

Examples

Run this code


if (FALSE) {

# Create an Interval-Data object containing the intervals of loan data
# (from the Kaggle Data Science platform) aggregated by loan purpose

LbyPIdt <- IData(LoansbyPurpose_minmaxDt,
                 VarNames=c("ln-inc","ln-revolbal","open-acc","total-acc")) 

print(LbyPIdt)

#Fit homoscedastic Gaussian mixtures with up to nine components

mclustres <- Idtmclust(LbyPIdt)
plotInfCrt(mclustres,legpos="bottomright")
print(mclustres)

#Display the results of the best mixture according to the BIC

summary(mclustres,parameters=TRUE,classification=TRUE)
pcoordplot(mclustres)

#Repeat the analysus with both homoscedastic and heteroscedastic mixtures up to six components

mclustres1 <- Idtmclust(LbyPIdt,G=1:6,Mxt="HomandHet")
plotInfCrt(mclustres1,legpos="bottomright")
print(mclustres1)

#Display the results of the best heteroscedastic mixture according to the BIC

summary(mclustres1,parameters=TRUE,classification=TRUE,model="HetG2C2")

}