wy

pca.r

wdata = read_excel("w.xlsx")
fit <- hclust(dist(model$x[,1:3]), method="complete") # 1:3 -> based on 3 components
groups <- cutree(fit, k=5)

library(rgl)
plotPCA <- function(x, nGroup) {
    n <- ncol(x) 
    if(!(n %in% c(2,3))) { # check if 2d or 3d
        stop("x must have either 2 or 3 columns")
    }

    fit <- hclust(dist(x), method="complete") # cluster
    groups <- cutree(fit, k=nGroup)

    if(n == 3) { # 3d plot
        plot3d(x, col=groups, type="s", size=1, axes=F)
        axes3d(edges=c("x--", "y--", "z"), lwd=3, axes.len=2, labels=FALSE)
        grid3d("x")
        grid3d("y")
        grid3d("z")
    } else { # 2d plot
        maxes <- apply(abs(x), 2, max)
        rangeX <- c(-maxes[1], maxes[1])
        rangeY <- c(-maxes[2], maxes[2])
        plot(x, col=groups, pch=19, xlab=colnames(x)[1], ylab=colnames(x)[2], xlim=rangeX, ylim=rangeY)
        lines(c(0,0), rangeX*2)
        lines(rangeY*2, c(0,0))
    }
}

plotPCA(model$x[,1:2], 5)