Biarrows: Adds correlation arrows to the scatterplot

In shipunov: Miscellaneous Functions from Alexey Shipunov

Adds correlation arrows to the scatterplot

Description

Plots 'orig' variables as arrows on the 'deriv' variables 2D scatterplot

Usage

Biarrows(deriv, orig, coeffs=NULL, shrink=0.45, closer=0.9,
 pt.col="forestgreen", pt.cex=1, pt.pch=NA, tx=colnames(orig),
 tx.col="forestgreen", tx.cex=0.8, tx.font=1, tx.pos=NULL, tx.off=0.5, xpd=TRUE,
 ar.col="forestgreen", ar.len=0.05, shift="auto", ...)

Arguments

deriv	Data derived from, e.g., dimension reducion of 'orig'
orig	Original data
coeffs	(Optional) two-column matrix with proposed coordinates of arrow tips, row names must represent 'orig' variables
shrink	How to shrink arrows in relation to 'deriv' ranges, default is 45% (0.45)
closer	How closer to the center (in relation to the text label) is the arrow tip, default is 0.9
pt.col	Color of points, default is "forestgreen"
pt.cex	Size of points, default is 1
pt.pch	Type of points, default is NA (no points)
tx	Text labels, default are 'colnames(orig)'
tx.col	Color of text labels, default is "forestgreen"
tx.cex	Size of text, default is 0.8
tx.font	Font of text, default is 1 (plain)
tx.pos	Position of text, default is NULL (in the center)
tx.off	Offest for text labels, default 0.5 (works only if 'tx.pos' is not NULL)
xpd	Allow text to go outside of plotting region?
ar.col	Color of arrows, default is "forestgreen"
ar.len	Length of the edges of the arrow head (in inches)
shift	Shift from the center which is c(0, 0); default is "auto" which is colMeans(deriv)
...	Further arguments to arrows()

Details

Biarrows() calculates correlations between two sets of variables which generally belong to the same data: more then one 'orig' variables and exactly two 'deriv' variables. These correlations might be understood as importances of the 'orig' variables. Then Biarrows() scales correlations to the 'deriv' ranges and adds text labels and arrows (possibly also points) to the scatterplot of derived variables. These arrows represent the original variables in relation with derived variables. Resulted plot may be seen as a biplot which simultaneously shows two sets of variables. In fact, it is possible to show three and more sets of variables (see examples).

This approach might work for data derived from (almost) any kind of dimensional reduction. Biarrows() is also much more flexible than standard biplot(). Please note, however, that Biarrows() is only visualization, and numerical conclustions might not be justified.

If 'deriv' data contains more then 2 variables, the rest will be discarded. Both 'deriv' and 'orig' should be either data frames or matrices with column names and compatible dimensions, possibly with NAs.

Biarrows(dr, coeffs=...) allows to use pre-calculated coefficients. In that case, 'data' is ignored (except for column names, but they might be supplied separately as 'tx' value), and 'coeffs' will be scaled. See examples to understand better how it works.

To suppress arrows or text, use zero color. Points are suppressed by default.

Author(s)

Alexey Shipunov

See Also

biplot

Examples

iris.cmd <- cmdscale(dist(iris[, -5]))
plot(iris.cmd, xlab="Dim 1", ylab="Dim 2")
Biarrows(iris.cmd, iris[, -5])
title(main="MDS biplot with Biarrows()")

## ===

library(MASS)
iris.mds <- isoMDS(dist(unique(iris[, -5])))
plot(iris.mds$points, xlab="Dim 1", ylab="Dim 2")
Biarrows(iris.mds$points, unique(iris[, -5]))
title(main="Non-metric MDS biplot with Biarrows()")

## ===

library(MASS)
iris.smm <- sammon(dist(unique(iris[, -5])))
plot(iris.smm$points, xlab="Dim 1", ylab="Dim 2")
Biarrows(iris.smm$points, unique(iris[, -5]))
title(main="Sammon mapping biplot with Biarrows()")

## ===

iris.p <- prcomp(iris[, -5], scale=TRUE)
biplot(iris.p, xpd=TRUE, main="Original PCA biplot")
plot(iris.p$x)
Biarrows(iris.p$x, iris[, -5])
title(main="PCA biplot with Biarrows()")

## ===

plot(iris.p$x, xlab="PCA1", ylab="PCA2")
## how to use 'coeffs'
## they also useful as surrogates of variable importances
(coeffs <- cor(iris[, -5], iris.p$x, method="spearman"))
Biarrows(iris.p$x, tx=rownames(coeffs), coeffs=coeffs)

## ===

plot(iris[, c(1, 3)])
Biarrows(iris[, c(1, 3)], iris.p$x)
title(main="\"Reversed biplot\"")

## ===

plot(iris[, c(1, 3)])
Biarrows(iris[, c(1, 3)], iris[, c(2, 4)])
title(main="Iris flowers: lengths vs. widths")

## ===

plot(iris.p$x)
Biarrows(iris.p$x[, 1:2], iris.p$x[, 1:2])
title(main="\"Self-biplot\" on PCA")

## ===

library(MASS)
iris.ldap <- predict(lda(Species ~ ., data=iris), iris[, -5])
plot(iris.ldap$x)
Biarrows(iris.ldap$x, iris[, -5])
Biarrows(iris.ldap$x, iris.p$x[, 1:2], shift=c(9, 2.5),
 shrink=0.95, lty=2, ar.col="darkgrey", tx.col="darkgrey")
title(main="Triplot: LDA, original variables and PCA axes")

## ===

iris.cl <- Classproj(iris[, -5], iris$Species)
plot(iris.cl$proj, col=iris$Species)
Biarrows(iris.cl$proj, iris[, -5])
title(main="Classproj biplot")

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