Description Usage Arguments Details Value Author(s) References See Also Examples
POICalc
Points Of Interest (POI) allows for the exploration of multidimensional data, by representing information according to its similarity with every POI defined for the set.
1 |
objeto |
Object of class POI |
NC |
Number of POI (points of interest as proposed by Costa and Venturini. See references. |
cx |
x coordinates |
cy |
x coordinates |
r |
Plot Radius |
... |
further arguments |
POIs are located on a circle, and data are displayed within this circle according to their similarities to these POI. Interactive actions are possible: selection, zoom, dynamical change of POI.
Pcoords |
Matrix with POIs coordinates |
PcoordsFI |
Matrix with POIs coordinates with fisheye effect applied. |
newPcoords |
Matrix with coordinates for the lines joining POIs |
objeto |
Matrix with coordinates for elements in the main set. |
Eduardo San Miguel Martin
Da Costa, David & Venturini, Gilles (2006). An Interactive Visualization Environment for Data Exploration Using Points of Interest. adma 2006: 416-423
Furnas, George (1986). Generalized Fisheye Views. Human Factors in computing systems, CHI '86 conference proceedings, ACM, New York, pp. 16-23.
Heidi Lam, Ronald A. Rensink, and Tamara Munzner (2006). Effects of 2D Geometric Transformations on Visual Memory. Proc. Applied Perception in Graphics and Visualization (APGV 2006), 119-126, 2006.
Keith Lau, Ron Rensink, and Tamara Munzner (2004). Perceptual Invariance of Nonlinear Focus+Context Transformations. Proc. First Symposium on Applied Perception in Graphics and Visualization (APGV 04) 2004, pp 65-72.
Lamping, J., Rao, R., Pirolli, P. (1995) A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. Proc. ACM Conf. Human Factors in Computing Systems, CHI. ACM. pp, 401-408
POIPlot-methods
,POI-class
,plotPOI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ## Not run:
## IRIS Example
data(iris)
# distance of each element to each dimension max and min
matrizSim = cbind(
1 - (max(iris[,1]) - iris[,1]) / (max(max(iris[,1]) - iris[,1])),
1 - (max(iris[,2]) - iris[,2]) / (max(max(iris[,2]) - iris[,2])),
1 - (max(iris[,3]) - iris[,3]) / (max(max(iris[,3]) - iris[,3])),
1 - (max(iris[,4]) - iris[,4]) / (max(max(iris[,4]) - iris[,4])),
1 - (min(iris[,1]) - iris[,1]) / (min(min(iris[,1]) - iris[,1])),
1 - (min(iris[,2]) - iris[,2]) / (min(min(iris[,2]) - iris[,2])),
1 - (min(iris[,3]) - iris[,3]) / (min(min(iris[,3]) - iris[,3])),
1 - (min(iris[,4]) - iris[,4]) / (min(min(iris[,4]) - iris[,4])))
# exaggerate diffs
matrizSim = matrizSim^3
# Create POI plot
irisPOI = POICreate('POI')
irisPOI@matrizSim <- matrizSim
irisPOI@wordsInQuery <- c('high.Sepal.Length', 'high.Sepal.Width', 'high.Petal.Length', 'high.Petal.Width',
'low.Sepal.Length', 'low.Sepal.Width', 'low.Petal.Length', 'low.Petal.Width')
POIcoords(irisPOI) <- POICalc(irisPOI ,length(irisPOI@wordsInQuery))
irisPOI@docs <- cbind(matrix(seq(1:nrow(irisPOI@objeto))),matrix(seq(1:nrow(irisPOI@objeto))))
irisPOI@colores <- c(rep(2,50),rep(3,50),rep(4,50))
try(rm('POI.env'), silent = T)
plotPOI(irisPOI)
## End(Not run)
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