focusedMDS: Focused, interactive multidimensional scaling
In focusedMDS: Focused, Interactive Multidimensional Scaling

Description Usage Arguments Examples

View source: R/focusedMDS.R

focusedMDS takes a distance matrix and plots it as an interactive graph. Double click on any point to choose a new focus point, and hover over points to see their ID labels. In this graph, one point is focused on at the center of the graph. All other points are plotted around this central point at their exact distances to the point, as given in the distance matrix. In other words, the distance between each point and the focus point are the true distances given in the distance matrix. The non focus points are plotted with respect to each other as exactly as possible. For more details, see https://lea-urpa.github.io/focusedMDS.html.

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focusedMDS(distances, ids = NULL, color_cat = NULL, focus_point = ids[1],
  size = NULL, circles = 7, tol = 0.001, check_matrix = FALSE,
  subsampling = FALSE, color_palette = NULL, title = NULL)

`distances`	A square, symmetric distance matrix or `dist` object.
`ids`	A vector with length equal to the number of rows of the matrix given in `distances`. Must be a character vector.
`color_cat`	A vector with length equal to the number of rows of the matrix given in `distances`. Content of the vector can be either numeric, factor, or character. Values will be assigned to color categories.
`focus_point`	The initial ID to be plotted at the center of the focusedMDS graph (default is the first element in the `ids` vector). Must be an element of the `ids` vector.
`size`	The fixed size of the focusedMDS graph, in pixels. Disables dynamic sizing.
`circles`	The number of background polar gridlines.
`tol`	The tolerance for the optimization method choosing the location of the non-focus points. Default 0.001.
`check_matrix`	Logical value permitting additional checks of the matrix, ensuring that the given matrix fulfills the triangle inequality. Slows down the initial graph plotting, but useful if you are not sure if your matrix is a distance matrix or has been calculated correctly.
`subsampling`	Logical value stating that for samples of over 100 points, each point iteratively plotted after the 100th point will be optimized to a subsample of the previously plotted data points. Recommended for plotting data sets with more than 300 points.
`color_palette`	Optional specification of a color palette to use when `colors` parameter is given. Must be a vector of CSS colors, with length at least as long as the number of unique variables in `colors`.
`title`	Optional title for plot, must be a single character string.

# See http://lea-urpa.github.io/focusedMDS.html for 
# an illustrated version of this example.

library(datasets)
library(focusedMDS)

# Load Edgar Anderson's Iris Data
data("iris")

# Create table of measures to compare individuals on
table <- iris[ , c("Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width")]

# Find euclidean distances based on these measures
dists <- dist(table)

# Simplest usage: only with dataset
focusedMDS(dists)

# Create labels based on flower species
colorvector <- as.vector(iris$Species)

colors <- c("firebrick", "cornflowerblue", "gold")

# Visualization with color labels
focusedMDS(dists, color_cat = colorvector, color_palette = colors )

# Create text labels
table(iris$Species)
names <- c(paste(rep("setosa", 50), 1:50, sep=""),
           paste(rep("versicolor", 50), 1:50, sep=""),
           paste(rep("virginica", 50), 1:50, sep=""))

focusedMDS(dists, ids = names, color_cat = colorvector, color_palette = colors)