pcloadhm: Heat map representation of principal component loadings of...
In monogeneaGM: Geometric Morphometric Analysis of Monogenean Anchors

Description Usage Arguments Details Author(s) References See Also Examples

This function produces a graphical representation of selected principal component loadings of shape variables in the form of a heat map.

1 2	pcloadhm(x, pc = 1, sgn = 1, nrow = 11, color.code = NULL, ylab = "Landmark", xlab = "Coordinate", yaxis = FALSE, tit = NULL)

`x`	a matrix containing the loadings of shape variables (row) of each principal component (column)
`pc`	a constant specifying the principal component of interest
`sgn`	the sign of the loadings; this value should follow the one used in `pca2d`
`nrow`	a constant indicating the number of landmarks defined in the anchors; defaults to 11
`color.code`	a character vector of hex color codes that define the color palette; if left undefined, defaults to the red-white-blue palette
`ylab`	y-axis title for the plot
`xlab`	x-axis title for the plot
`yaxis`	if TRUE, the y-axis values are labelled
`tit`	title for the plot

The sign and magnitude of loadings of shape variables for a particular principal component is important for the latter's biological interpretation. A heat map representation is an alternative to the usual manner of presenting them in tabular form, and may be more effective for presentation purpose. Reference to the circular plots (plotCircular) for each landmark and the PCA plots can be very useful in determining the biological interpretation of a particular principal component.

Tsung Fei Khang tfkhang@um.edu.my

Khang TF, Soo OYM, Tan WB, Lim LHS. (2016). Monogenean anchor morphometry: systematic value, phylogenetic signal, and evolution. PeerJ 4:e1668.

plotCircular, pca2d, colorBar

library(phytools)

data(ligophorus_shape)
data(ligotree)
data(spcolmap)

shapev <- pca2d(ligophorus_shape[,1:22], sgn=1, labcol=spcolmap$color,
phylo=TRUE, phy=ligotree, genus="L. ",
bound.y = c(-0.1, 0.1), bound.x1 =c(-0.15,0.2), bound.x2=c(-0.15,0.2))

fff <- c(0,1,1,2,2,3,3,0,4,4)
nf <- layout(matrix(c(rep(0,length(fff)),rep(fff,5),rep(0,length(fff))),
7,length(fff),byrow=TRUE))
layout.show(nf)
par(mar=c(5,4,4,1))
#the loadings for the first three PC of shape variables of the ventral anchors
pcloadhm(shapev$variable,sgn=1,pc=1,yaxis=TRUE,tit="VPC1")
pcloadhm(shapev$variable,sgn=1,pc=2,ylab="", tit="VPC2")
pcloadhm(shapev$variable,sgn=1,pc=3,ylab="", tit="VPC3")

#add a colorbar for completeness
par(mar=c(5,2,4,3))
colorBar(min=-1, max=1)