MDSv: MDS: dimension importance ("explained variance" surrogate)

In shipunov: Miscellaneous Functions from Alexey Shipunov

MDS: dimension importance ("explained variance" surrogate)

Description

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

Usage

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.

Value

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

shipunov documentation built on Feb. 16, 2023, 9:05 p.m.