"plot"(x, pc.axes=NULL, pch=16, col=par("col"), cex=0.8, mar=c(4, 4, 1, 1),...)"plot"(x, y = NULL, type = "o", pch = 18, main = "", sub = "", xlim = c(0, 20), ylim = NULL, ylab = "Proporton of Variance (%)", xlab = "Eigenvalue Rank", axes = TRUE, ann = par("ann"), col = par("col"), lab = TRUE, ...) "plot"(x, inds=NULL, col=rainbow(nrow(x)), lab = "", ...)
pca.xyz
. Produces a z-score plot (conformer plot) and an eigen spectrum plot (scree plot).
plot.pca
is a wrapper calling both plot.pca.score
and
plot.pca.scree
resulting in a 2x2 plot with three score plots
and one scree plot.
Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.
attach(transducin)The following objects are masked from transducin (pos = 3): annotation, core, pdbspc.xray <- pca(pdbs$xyz, rm.gaps=TRUE)NOTE: Removing 49 gap positions with missing coordinate data retaining 305 non-gap positions for analysis.plot(pc.xray) ## Color plot by nucleotide state vcolors <- annotation[, "color"]plot(pc.xray, col=vcolors)## Focus on a single plot of PC1 vs PC2 x <- plot(pc.xray, pc.axes=1:2, col=vcolors)## Identify points interactively with mouse clicks #identify(x, labels=basename.pdb(pdbs$id)) ## Add labels to select points inds <- c(1,10,37) text(x[inds,], labels=basename.pdb(pdbs$id[inds]), col="blue")## Alternative labeling method #labs <- rownames(annotation) #inds <- c(2,7) #plot.pca.score(pc.xray, inds=inds, col=vcolors, lab=labs) ## color by seq identity groupings #ide <- seqidentity(pdbs$ali) #hc <- hclust(as.dist(1-ide)) #grps <- cutree(hc, h=0.2) #vcolors <- rainbow(max(grps))[grps] #plot.pca.score(pc.xray, inds=inds, col=vcolors, lab=labs) detach(transducin)