Description Usage Arguments Value Author(s) References Examples
With the parameters resulting from fitting a nominal logistic model to the row scores for a given variable, the function calculates all the information necessary to plot the tessellation generated by the fit. The final user will not normally use this function.
1 |
beta |
Matrix with the estimated parameters for a given nominal variable. It has as many rows as the number of categories minus one and three columns (one for the constant and other two for the x-y coordinates on the plane). |
An object of class "voronoiprob"
. This has the components:
x |
x-coordinates for the real points (Vertices of the tessellation). |
y |
y-coordinates for the real points (Vertices of the tessellation). |
n1 |
vector with the first neighbours of the real points |
n2 |
vector with the second neighbours of the real points |
n3 |
vector with the third neighbours of the real points |
dummy.x |
x-coordinates for the dummy points |
dummy.y |
y-coordinates for the dummy points |
ndummy |
Number of dummies |
IndReal |
Matrix with the indices of each real point in the tessellation |
Centers |
Matrix with the points resulting from inverting the tessellation |
hideCat |
Vector to indicate if there are some hidden categories |
equivRegiones |
Matrix with the new re-numbered categories (when some are hidden) |
Julio Cesar Hernandez Sanchez, Jose Luis Vicente-Villardon
Maintainer: Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Hern\'andez S\'anchez, J. C., & Vicente-Villard\'on, J. L. (2013). Logistic biplot for nominal data. arXiv preprint arXiv:1309.5486.
Gower, J. & Hand, D. (1996), Biplots, Monographs on statistics and applied probability 54. London: Chapman and Hall., 277 pp.
Evans, D. & Jones, S. (1987), Detecting voronoi (area of influence) polygons ,Mathematical Geology 19(6), 523–537.
Hartvigsen, D. (1992), Recognizing voronoi diagrams with linear programming, ORSA Journal on Computing 4, 369–374.
Schoenberg, F., Ferguson, T. & Li, C. (2003), Inverting dirichlet tesselations, Computer journal 46(1), 76–83.
1 2 3 4 5 6 | data(HairColor)
data = data.matrix(HairColor)
xEM = NominalLogBiplotEM(data, dim = 2,showResults = FALSE)
nomreg = polylogist(data[,2],xEM$RowCoordinates[,1:2],penalization=0.1)
tesselation = Generators(nomreg$beta)
tesselation
|
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