R/plot.summarizedvc.R

In jirayusjiar/Jmisc:

plot.summarizedvc <- function(vc,
                              legend = c('Non-correlated metrics', 'Correlated metrics'),
                              abbrev = FALSE,
                              legend. = FALSE,
                              loc,
                              maxlen = 20,
                              labels = NULL,
                              ...){

    non.correlated.metrics <- AutoSpearman(vc$dataset, vc$indep)
    correlated.metrics <- vc$indep[!vc$indep %in% non.correlated.metrics]
    trans <- vc$trans
    s <- c(
        hoeffding = "30 * Hoeffding D",
        spearman = switch(
            trans,
            square = expression(paste(Spearman, ~
                                          rho ^ 2)),
            abs = expression(paste(Spearman,
                                   ~
                                       abs(rho))),
            none = expression(paste(Spearman,
                                    ~
                                        rho))
        ),
        pearson = switch(
            trans,
            square = expression(paste(Pearson,
                                      ~
                                          r ^ 2)),
            abs = expression(paste(Pearson, ~ abs(r))),
            none = expression(paste(Pearson, ~
                                        r))
        ),
        bothpos = "Proportion",
        ccbothpos = "Chance-Corrected Proportion"
    )[vc$similarity]
    if ((is.expression(s) &&
         as.character(s) == "NULL") ||
        (!is.expression(s) && (is.na(s) || s == "")))
        s <- vc$similarity
    ylab <- s

    if (legend.)
        abbrev <- TRUE
    if (!length(labels))
        labels <- dimnames(vc$sim)[[2]]
    olabels <- labels
    if (abbrev)
        labels <- abbreviate(labels)
    if (!length(vc$hclust))
        stop("clustering was not done on similarity=\"ccbothpos\"")

    # Modify from
    # https://cran.r-project.org/web/packages/dendextend/vignettes/FAQ.html
    # set3 ####
    dend <- as.dendrogram(vc$hclust, hang = -1)
    ifill = c('darkgreen', 'red')
    text.color <-
        ifelse(labels(dend) %in% correlated.metrics, 'red', 'darkgreen')
    dend <-
        assign_values_to_leaves_edgePar(dend = dend,
                                        value = text.color,
                                        edgePar = "col")

    # max pixels need to plot metrics
    max.length <-
        max(unlist(lapply(indep, function(x)
            strwidth(x, font = 12, units = 'in'))))
    tick.margin <- (5 + (4.544286 * (max.length - 0.574)))
    par(mar = c(5, 2.5, 5, tick.margin))
    #dend %>% hang.dendrogram(hang_height = 0.2) %>%
    plot(
        dend,
        main = "",
        ylab = '',
        yaxt = 'n',
        labels = labels,
        ann = FALSE,
        axes = FALSE,
        hang = -1,
        horiz = T
    )
    xa <- pretty(range(1 - vc$hclust$height))
    axis(1, at = 1 - xa, labels = format(xa))
    title(xlab = ylab)
    s <- labels != olabels
    if (legend. && any(s)) {
        if (missing(loc)) {
            cat("Click mouse at upper left corner of legend\n")
            loc <- locator(1)
        }
        olabels <- ifelse(nchar(olabels) > maxlen,
                          substring(olabels,
                                    1, maxlen),
                          olabels)
        text(loc, paste(paste(labels[s], ":", olabels[s], "\n"),
                        collapse = ""), adj = 0)
    }
    abline(v = (1 - varclus.threshold))
    legend(
        'top',
        legend = legend,
        fill = ifill,
        xpd = TRUE,
        inset = c(0, -0.1)
        # inset = c(0, (-0.1 - (
        #   0.006 * (bottomMargin - 5)
        # )))
    )
    # set2####
    invisible()

}