do.radviz {Radviz}R Documentation

Projects a Matrix or a Data Frame to a 2D space defined by Dimensional Anchors

Description

do.radviz will return a projection of a multidimensional dataset onto a 2D space defined by dimensional anchors that have been projected on the unit circle using make.S

Usage

do.radviz(
  x,
  springs,
  trans = do.L,
  scaling = 1,
  label.color = "orangered4",
  label.size = NA,
  type = NULL,
  graph = NULL
)

Arguments

x

a data.frame or matrix to be projected, with column names matching row names in springs

springs

a matrix of 2D dimensional anchor coordinates, as returned by make.S

trans

a transformation to be applied to the data before projection

scaling

a scaling factor applied to data before the projection.

label.color

deprecated, use plot.radviz instead

label.size

deprecated, use plot.radviz instead

type

character string specifying the method used for obtaining the springs. Current methods are: Radviz, Freeviz and Graphviz. When not provided, type is derived from the other inputs

graph

igraph object (only relevant for result obtained from do.optimGraphviz analysis)

Details

The function expects that at least some of the column names in x will be matched by all row names in springs. The scaling factor can be used to increase the distance between points, making it useful in situations where all points are pulled together either because of similar values or large number of channels. The scaling is applied **after** the transformation by trans. The scaling idea is taken from [Artur & Minghim 2019](https://doi.org/10.1016/j.cag.2019.08.015).

Value

an object of class radviz with the following slots:

Author(s)

Yann Abraham

Examples

data(iris)
das <- c('Sepal.Length','Sepal.Width','Petal.Length','Petal.Width')
S <- make.S(das)
rv <- do.radviz(iris,S)
summary(rv)
data(iris)
das <- c('Sepal.Length','Sepal.Width','Petal.Length','Petal.Width')
iris0 <- rbind(iris,c(rep(0,length(das)),NA))
S <- make.S(das)
rv0 <- do.radviz(iris0,S)

sum(!is.valid(rv0)) # should be 1

# to find which points where invalid in the data
which(!is.valid(rv0))

# to review the original data points
rv1 <- subset(rv0,is.valid(rv0))

summary(rv1)

[Package Radviz version 0.9.3 Index]