R: Class IdtMclust

IdtMclust-class {MAINT.Data}

R Documentation

Class IdtMclust

Description

IdtMclust contains the results of fitting mixtures of Gaussian distributions to interval data represented by objects of class IData.

Slots

call:

The matched call that created the IdtMclust object

data:

The IData data object

NObs:

Number of entities under analysis (cases)

NIVar:

Number of interval variables

SelCrit:

The model selection criterion; currently, AIC and BIC are implemented

Hmcdt:

Indicates whether the optimal model corresponds to a homoscedastic (TRUE) or a hetereocedasic (FALSE) setup

BestG:

The optimal number of mixture components.

BestC:

The configuration case of the variance-covariance matrix in the optimal model

logLiks:

The logarithms of the likelihood function for the different models tried

logLik:

The logarithm of the likelihood function for the optimal model

AICs:

The values of the AIC criterion for the different models tried

aic:

The value of the AIC criterion for the he optimal model

BICs:

The values of the BIC criterion for the different models tried

bic:

The value of the BIC criterion for the he optimal model

parameters

A list with the following components:

pro: A vector whose kth component is the mixing proportion for the kth component of the mixture model.
mean: The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
covariance: A three-dimensional array with the covariance estimates. If Hmcdt is FALSE (heteroscedastic setups) the third dimension levels run through the BestG mixture components, with one different covariance matrix for each level. Otherwise (homoscedastic setups), there is only one covariance matrix and the size of the third dimension equals one.

z:

A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class.

classification:

The classification corresponding to z, i.e. map(z).

allres:

A list with the detailed results for all models fitted.

Methods

show: signature(object = "IdtMclust"): show S4 method for the IdtMclust-class
summary: signature(object = "IdtMclust"): summary S4 method for the IdtMclust-class
parameters: signature(x = "IdtMclust"): retrieves the value of the parameter estimates for the obtained partition
pro: signature(x = "IdtMclust"): retrieves the value of the estimated mixing proportions for the obtained partition
mean: signature(x = "IdtMclust"): retrieves the value of the component means for the obtained partition
var: signature(x = "IdtMclust"): retrieves the value of the estimated covariance matrices for the obtained partition
cor: signature(x = "IdtMclust"): retrieves the value of the estimated correlation matrices
classification: signature(x = "IdtMclust"): retrieves the individual class assignments for the obtained partition
SelCrit: signature(x = "IdtMclust"): retrieves a string specifying the criterion used to find the best model and partition
Hmcdt: signature(x = "IdtMclust"): returns TRUE if an homecedastic model has been assumed, and FALSE otherwise
BestG: signature(x = "IdtMclust"): returns the number of components selectd
BestC: signature(x = "IdtMclust"): retruns the covariance configuration selected
PostProb: signature(x = "IdtMclust"): retrieves the estimates of the individual posterir probabilities for the obtained partition
BIC: signature(x = "IdtMclust"): returns the value of the BIC criterion
AIC: signature(x = "IdtMclust"): returns the value of the AIC criterion
logLik: signature(x = "IdtMclust"): returns the value of the log-likelihood

Author(s)

Pedro Duarte Silva <psilva@porto.ucp.pt>
Paula Brito <mpbrito.fep.up.pt>

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.