MDSv function

MDS: dimension importance ("explained variance" surrogate)

Calculates R-squared coefficients of the linear relationships between each of derived variables and original data

MDSv(scores)

Arguments

• scores: Data frame or matrix with values (e.g., result of isoMDS())

Details

MDSv() converts each of the derived variables and original data into distance matrices, and then uses lm() to calculate adjusted R-squared coefficients. These coefficients may be used to understand the "importance" of each new dimension. They work for any dimension reduction techique including multidimensional scaling.

Returns

Numeric vector, one values per column of scores

Author(s)

Alexey Shipunov

Examples

iris.dist <- dist(unique(iris[, -5]), method="manhattan")

iris.cmd <- cmdscale(iris.dist)
MDSv(iris.cmd)

iris.p <- prcomp(iris[, -5])
MDSv(iris.p$x)
100*summary(iris.p)$importance[2, ] # compare with MDSv() results