MAINT.Data (version 2.6.2)

plotInfCrt-methods: Information criteria plot.

Description

Method plotInfCrt displays a plot representing the values of an appropriate information criterion (currently either BIC or AIC) for the models whose results are stored in an IdtMclust-method object. A supplementary short output message prints the values of the chosen criterion for the 'nprin' best models.

Usage

# S4 method for IdtMclust
plotInfCrt(object, crt=object@SelCrit, legpos="right", nprnt=5,
  legendout=TRUE, outlegsize="adjstoscreen", outlegdisp="adjstoscreen", 
  legendpar=list(), ...)

Arguments

object

An object of type “IdtMclust” representing the the clusterig results of an Interval-valued data set obtained by the function “IdtMclust”.

crt

The information criteria whose values are to be displayed.

legpos

Legend position. Alternatives are “right” (default), “left”, “bottomright”, “bottomleft”, “topright” and “topleft” .

nprnt

Number of solutions for which the value of the information criterio should be printed in an suplmentary short output message.

legendout

A boolean flag indicating if the legend should be placed outside (default) or inside the main plot.

outlegsize

The size (in inches) to be reserved for a legend placed outside the main plot, or the string “adjstoscreen” (default) for an automatic adjustment of the plot and legend sizes.

outlegdisp

The displacement (as a percentage of the main plot size) of the outer margin for a legend placed outside the main plot, or the string “adjstoscreen” (default) for an automatic adjustment of the legend position.

legendpar

A named list with graphical parameters for the plot legend.

...

Graphical arguments to be passed to methods.

See Also

IdtMclust, Idtmclust, pcoordplot

Examples

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")) 

#Fit homoscedastic and heteroscedastic mixtures up to Gaussian mixtures with up to seven components

mclustres <- Idtmclust(LbyPIdt,G=1:7,Mxt="HomandHet")

#Compare de model fit according to the BIC

plotInfCrt(mclustres,legpos="bottomleft")

#Display the results of the best three mixtures according to the BIC

summary(mclustres,parameters=TRUE,classification=TRUE)
pcoordplot(mclustres)
summary(mclustres,parameters=TRUE,classification=TRUE,model="HetG2C2")
summary(mclustres,parameters=TRUE,classification=TRUE,model="HomG6C1")
pcoordplot(mclustres,model="HomG6C1")


}