Plot.plsda: PLS plots

In muma: Metabolomics Univariate and Multivariate Analysis

Description

Visualizes and write PLS score and w*c plots.

Usage

Plot.plsda(pcx, pcy, scaling)

Arguments

pcx	an integer indicating the principal component to be plotted in x
pcy	an integer indicating the principal component to be plotted in y
scaling	a character string indicating the name of the scaling previously specified in the function 'explore.data'

Details

Score and w*c plots are graphically visualized and written in the directory 'PLS-DA'.

Author(s)

Edoardo Gaude, Dimitrios Spiliotopoulos, Francesca Chignola, Silvia Mari, Andrea Spitaleri and Michela Ghitti

Examples

## The function is currently defined as
function (pcx, pcy, scaling) 
{
    pwd.score = paste(getwd(), "/PLS-DA_", scaling, "/PLSDA_Scores_", 
        scaling, ".csv", sep = "")
    score = read.csv(pwd.score, header = TRUE)
    score.x = score[, -1]
    rownames(score.x) = score[, 1]
    pwd.load = paste(getwd(), "/PLS-DA_", scaling, "/PLSDA_Loadings_", 
        scaling, ".csv", sep = "")
    loading = read.csv(pwd.load, header = TRUE)
    loading.x = loading[, -1]
    rownames(loading.x) = loading[, 1]
    pwd.p = paste(getwd(), "/PLS-DA_", scaling, "/PLSDA_P_", 
        scaling, ".csv", sep = "")
    p = read.csv(pwd.p, header = TRUE)
    p.x = matrix(p[, -1], ncol = 1)
    pwd.ptot = paste(getwd(), "/PLS-DA_", scaling, "/PLSDA_Ptot_", 
        scaling, ".csv", sep = "")
    p = read.csv(pwd.ptot, header = TRUE)
    pvar.a = p.x[pcx, ]/p
    pvar.b = p.x[pcy, ]/p
    pvar.ai = round(pvar.a * 100, 1)
    pvar.bi = round(pvar.b * 100, 1)
    cum = pvar.ai + pvar.bi
    xlab = paste("Component", pcx, "(", pvar.ai, "%)", sep = "")
    ylab = paste("Component", pcy, "(", pvar.bi, "%)", sep = "")
    max.pc1 = 1.3 * (max(abs(score.x[, pcx])))
    max.pc2 = 1.3 * (max(abs(score.x[, pcy])))
    lim = c()
    if (max.pc1 > max.pc2) {
        lim = c(-max.pc1, max.pc1)
    }
    else {
        lim = c(-max.pc2, max.pc2)
    }
    pwdK = paste(getwd(), "/Preprocessing_Data_", scaling, "/class.csv", 
        sep = "")
    k = read.csv(pwdK, header = TRUE)
    k.s = k[order(k[, 2]), ]
    tutticolors = matrix(c(1, 2, 3, 4, 5, 6, 7, 8, "rosybrown4", 
        "green4", "navy", "purple2", "orange", "pink", "chocolate2", 
        "coral3", "khaki3", "thistle", "turquoise3", "palegreen1", 
        "moccasin", "olivedrab3", "azure4", "gold3", "deeppink"), 
        ncol = 1)
    col = c()
    for (i in 1:nrow(k.s)) {
        col = c(col, tutticolors[k.s[i, 2], ])
    }
    plot(score.x[, pcx], score.x[, pcy], col = col, pch = 19, 
        xlab = c(xlab), ylab = c(ylab), xlim = lim, ylim = lim, 
        sub = paste("Cumulative Proportion of Variance Explained = ", 
            cum, "%", sep = ""), main = paste("PLS-DA Score Plot (", 
            scaling, ")", sep = ""))
    text(score.x[, pcx], score.x[, pcy], col = col, cex = 0.5, 
        labels = rownames(score.x), pos = 1)
    axis(1, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey", 
        lwd = 0.7)
    axis(2, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey", 
        lwd = 0.7)
    library(car)
    dataEllipse(score.x[, pcx], score.x[, pcy], levels = c(0.95), 
        add = TRUE, col = "black", lwd = 0.4, plot.points = FALSE, 
        center.cex = 0.2)
    dirout = paste(getwd(), "/PLS-DA_", scaling, "/", sep = "")
    scor = paste(dirout, "ScorePlot_PLS-DA_", scaling, ".pdf", 
        sep = "")
    dev.copy2pdf(file = scor)
    Max.pc1 = 1.1 * (max(loading.x[, pcx]))
    Min.pc1 = 1.1 * (min(loading.x[, pcx]))
    Mpc1 = c(Min.pc1, Max.pc1)
    Max.pc2 = 1.1 * (max(loading.x[, pcy]))
    Min.pc2 = 1.1 * (min(loading.x[, pcy]))
    Mpc2 = c(Min.pc2, Max.pc2)
    dev.new()
    plot(loading.x[, pcx], loading.x[, pcy], xlim = Mpc1, ylim = Mpc2, 
        xlab = paste("w*c values ", pcx, sep = ""), ylab = paste("w*c values ", 
            pcy, sep = ""), main = paste("PLS-DA Loading Plot (", 
            scaling, ")", sep = ""))
    text(loading.x[, pcx], loading.x[, pcy], labels = rownames(loading.x), 
        cex = 0.7, pos = 1)
    axis(1, at = Mpc1 * 2, pos = c(0, 0), labels = FALSE, col = "grey", 
        lwd = 0.7)
    axis(2, at = Mpc2 * 2, pos = c(0, 0), labels = FALSE, col = "grey", 
        lwd = 0.7)
    load = paste(dirout, "W*cPlot_PLS-DA_", scaling, ".pdf", 
        sep = "")
    dev.copy2pdf(file = load)
  }

muma documentation built on May 2, 2019, 9:45 a.m.