NominalLogisticBiplot (version 0.2)

NominalLogisticBiplot-package: Nominal Logistic Biplot representations for polytomous data

Description

Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB) according to Hernandez-Sanchez & Vicente-Villardon (2013). The NLB procedure extends the binary logistic biplot to nominal (polytomous) data. The individuals are represented as points on a plane and the variables are represented as convex prediction regions rather than vectors as in a classical or binaly biplot. Using the methods from the Computational Geometry, the set of prediction regions is converted to a set of points in such a way that the prediction for each individual is established by its closest "category point". Then interpretation is based on distances rather than on projections. In this package we implement the geometry of such a representation and construct computational algorithms for the estimation of parameters and the calculation of prediction regions

Details

Package:

NominalLogisticBiplot

Type:

Package

Version:

1.0

Date:

2013-08-05

License:

GPL (>=2)

See Also

NominalLogisticBiplot,NominalLogBiplotEM,multiquad,summary.nominal.logistic.biplot,plot.nominal.logistic.biplot

Examples

data(HairColor)
nlbo = NominalLogisticBiplot(HairColor,sFormula=NULL,numFactors=2,
method="EM",penalization=0.2,show=FALSE)
summary(nlbo)
plot(nlbo,QuitNotPredicted=TRUE,ReestimateInFocusPlane=TRUE,
planex = 1,planey = 2,proofMode=TRUE,LabelInd=TRUE,AtLeastR2 = 0.01
,xlimi=-1.5,xlimu=1.5,ylimi=-1.5,ylimu=1.5,linesVoronoi = TRUE
,SmartLabels = FALSE, PlotInd=TRUE,CexInd = c(0.6,0.7,0.5,0.4,0.5,0.6,0.7)
,PchInd = c(1,2,3,4,5,6,7),ColorInd="black",PlotVars=TRUE,LabelVar = TRUE
,PchVar = c(1,2,3,4,5),ColorVar = c("red","black","yellow","blue","green")
,ShowResults=TRUE)