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Geomorph users,

Our function plotTangentSpace() performs a Principal Components Analysis (PCA) of shape variation and plots two dimensions of tangent space for a set of Procrustes-aligned specimens and also returns the shape changes associated with the two plotted principal axes. We purposefully restricted the options for this function because plotting in R has almost endless possibilities. That is why we added the ‘verbose=TRUE‘ option, so that the pc scores and pc shapes could be plotted on their own. And users are of course expected to do this for their publications and reports.

In this week’s exercise, we explore a few options to do advance of plotting PCAs and TPS grids with geomorph and R base functions.

Exercise 7 – Plotting Principal Components Analysis Graphs with TPS grids.

Publications of geometric morphometric data usually have at least one figure showing the results of a PCA and accompanying shape changes for the plotted axes. For example:

To make this graph:

Let’s use the plethodon data example
library(geomorph)
data(plethodon)
Y.gpa<-gpagen(plethodonland) #GPA-alignment We’ll make a factor out of both the species and site classifiers, creating a factor with four levels: gp <- as.factor(paste(plethodonspecies, plethodon$site)) [1] Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp [10] Jord Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp [19] Teyah Symp Teyah Symp Jord Allo Jord Allo Jord Allo Jord Allo Jord Allo Jord Allo Jord Allo [28] Jord Allo Jord Allo Jord Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo [37] Teyah Allo Teyah Allo Teyah Allo Teyah Allo Levels: Jord Allo Jord Symp Teyah Allo Teyah Symp Let’s create a colour vector to colour the groups, e.g. using rainbow() col.gp <- rainbow(length(levels(gp))) # generates a set of different colour over the rainbow spectrum [1] “#FF0000FF” “#80FF00FF” “#00FFFFFF” “#8000FFFF” You can also assign the names by hand using HEX codes or names of colours. This vector needs to have dimension labels names(col.gp) <- levels(gp) Using match()we can generates a vector of length(n) assigning a colour to each specimen col.gp <- col.gp[match(gp, names(col.gp))] Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp Jord Symp “#80FF00FF” “#80FF00FF” “#80FF00FF” “#80FF00FF” “#80FF00FF” “#80FF00FF” “#80FF00FF” “#80FF00FF” Jord Symp Jord Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp Teyah Symp “#80FF00FF” “#80FF00FF” “#8000FFFF” “#8000FFFF” “#8000FFFF” “#8000FFFF” “#8000FFFF” “#8000FFFF” Teyah Symp Teyah Symp Teyah Symp Teyah Symp Jord Allo Jord Allo Jord Allo Jord Allo “#8000FFFF” “#8000FFFF” “#8000FFFF” “#8000FFFF” “#FF0000FF” “#FF0000FF” “#FF0000FF” “#FF0000FF” Jord Allo Jord Allo Jord Allo Jord Allo Jord Allo Jord Allo Teyah Allo Teyah Allo “#FF0000FF” “#FF0000FF” “#FF0000FF” “#FF0000FF” “#FF0000FF” “#FF0000FF” “#00FFFFFF” “#00FFFFFF” Teyah Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo Teyah Allo “#00FFFFFF” “#00FFFFFF” “#00FFFFFF” “#00FFFFFF” “#00FFFFFF” “#00FFFFFF” “#00FFFFFF” “#00FFFFFF” run the plotTangentSpace function and save the results to PCA object PCA <- plotTangentSpace(Y.gpa$coords, verbose = T)

PCA is a list containing the pc.summary
PCA$pc.summary$importance
PC1        PC2        PC3        PC4        PC5        PC6        PC7
Standard deviation     0.04307484 0.03958025 0.02034959 0.01509451 0.01313772 0.01293281 0.01244592
Proportion of Variance 0.36743000 0.31023000 0.08201000 0.04512000 0.03418000 0.03312000 0.03068000

Cumulative Proportion  0.36743000 0.67767000 0.75967000 0.80479000 0.83897000 0.87209000 0.90277000

which can be used to make the new axis labels
xlab <- paste("Principal Component 1 ", "(", round(PCA$pc.summary$importance[2,1]*100, 1), "%)", sep="")
ylab <- paste("Principal Component 2 ", "(", round(PCA$pc.summary$importance[2,2]*100, 1), "%)", sep="")

We will use the advanced plotting functions layout() and par() to build up this multipart figure
mat <- matrix(c(4,5,0,1,1,2,1,1,3), 3)
[,1] [,2] [,3]
[1,]    4    1    1
[2,]    5    1    1
[3,]    0    2    3

With this function, we have divided up the plotting window into a 3×3 grid, and the numbers correspond to the order and location of each item being plotted

layout(mat, widths=c(1,1,1), heights=c(1,1,0.6))# set the size of the rows and columns

# Item 1 to plot, the graph of PC1 vs PC2
par(mar=c(4, 4, 1, 1)) # sets the margins

PCA also contains the pc scores in PCA$pc.scores. Here we take the first two columns for PC1 and PC2 plot(PCA$pc.scores[,1], PCA$pc.scores[,2], pch=21, cex=2, bg=col.gp, xlab=xlab, ylab=ylab, asp=T) legend(-0.09, 0.07, legend= unique(gp), pch=19, col=unique(col.gp)) Finally we add the TPS grids to demonstrate the shape changes along each axis. PCA contains the shape matrices PCA$pc.shapes.
ref <- mshape(Y.gpa$coords)# assign mean shape for use with plotRefToTarget below # Item 2 to plot, the first TPS grid; here we use the outline option to add to the visualisation par(mar = c(0,0,0,0)) # sets the margins plotRefToTarget(ref,PCA$pc.shapes$PC1min,outline=plethodon$outline)
# Item 3
plotRefToTarget(ref,PCA$pc.shapes$PC1max,outline=plethodon$outline) # Item 4 plotRefToTarget(ref,PCA$pc.shapes$PC2min,outline=plethodon$outline)
# Item 5
plotRefToTarget(ref,PCA$pc.shapes$PC2max,outline=plethodon\$outline)