tests/test2.R

In kkdey/CorShrink: Adaptive Shrinkage of Correlation and Covariance Matrices

#' Visualization of a correlation matrix using ggplot2
#' @import ggplot2
#' @description \itemize{ \item ggcorrplot(): A graphical display of a
#'   correlation matrix using ggplot2. \item cor_pmat(): Compute a correlation
#'   matrix p-values. }
#' @param corr the correlation matrix to visualize
#' @param method character, the visualization method of correlation matrix to be
#'   used. Allowed values are "square" (default), "circle".
#' @param type character, "full" (default), "lower" or "upper" display.
#' @param ggtheme function, ggplot2 theme name. Default value is theme_minimal.
#'   Allowed values are the official ggplot2 themes including theme_gray,
#'   theme_bw, theme_minimal, theme_classic, theme_void, ....
#' @param title character, title of the graph.
#' @param show.legend logical, if TRUE the legend is displayed.
#' @param legend.title a character string for the legend title. lower
#'   triangular, upper triangular or full matrix.
#' @param show.diag logical, whether display the correlation coefficients on the
#'   principal diagonal.
#' @param colors a vector of 3 colors for low, mid and high correlation values.
#' @param outline.color the outline color of square or circle. Default value is
#'   "gray".
#' @param hc.order logical value. If TRUE, correlation matrix will be hc.ordered
#'   using hclust function.
#' @param hc.method the agglomeration method to be used in hclust (see ?hclust).
#' @param lab logical value. If TRUE, add correlation coefficient on the plot.
#' @param lab_col,lab_size size and color to be used for the correlation
#'   coefficient labels. used when lab = TRUE.
#' @param p.mat matrix of p-value. If NULL, arguments sig.level, insig, pch,
#'   pch.col, pch.cex is invalid.
#' @param sig.level significant level, if the p-value in p-mat is bigger than
#'   sig.level, then the corresponding correlation coefficient is regarded as
#'   insignificant.
#' @param insig character, specialized insignificant correlation coefficients,
#'   "pch" (default), "blank". If "blank", wipe away the corresponding glyphs;
#'   if "pch", add characters (see pch for details) on corresponding glyphs.
#' @param pch add character on the glyphs of insignificant correlation
#'   coefficients (only valid when insig is "pch"). Default value is 4.
#' @param pch.col,pch.cex the color and the cex (size) of pch (only valid when
#'   insig is "pch").
#' @param tl.cex,tl.col,tl.srt the size, the color and the string rotation of
#'   text label (variable names).
#' @return
#' \itemize{
#'  \item ggcorrplot(): Returns a ggplot2
#'  \item cor_pmat(): Returns a matrix containing the p-values of correlations
#'  }
#' @examples
#' # Compute a correlation matrix
#' data(mtcars)
#' corr <- round(cor(mtcars), 1)
#' corr
#'
#' # Compute a matrix of correlation p-values
#' p.mat <- cor_pmat(mtcars)
#' p.mat
#'
#' # Visualize the correlation matrix
#' # --------------------------------
#' # method = "square" or "circle"
#' ggcorrplot(corr)
#' ggcorrplot(corr, method = "circle")
#'
#' # Reordering the correlation matrix
#' # --------------------------------
#' # using hierarchical clustering
#' ggcorrplot(corr, hc.order = TRUE, outline.col = "white")
#'
#' # Types of correlogram layout
#' # --------------------------------
#' # Get the lower triangle
#' ggcorrplot(corr, hc.order = TRUE, type = "lower",
#'      outline.col = "white")
#' # Get the upeper triangle
#' ggcorrplot(corr, hc.order = TRUE, type = "upper",
#'      outline.col = "white")
#'
#' # Change colors and theme
#' # --------------------------------
#' # Argument colors
#' ggcorrplot(corr, hc.order = TRUE, type = "lower",
#'    outline.col = "white",
#'    ggtheme = ggplot2::theme_gray,
#'    colors = c("#6D9EC1", "white", "#E46726"))
#'
#' # Add correlation coefficients
#' # --------------------------------
#' # argument lab = TRUE
#' ggcorrplot(corr, hc.order = TRUE, type = "lower",
#'    lab = TRUE)
#'
#' # Add correlation significance level
#' # --------------------------------
#' # Argument p.mat
#' # Barring the no significant coefficient
#' ggcorrplot(corr, hc.order = TRUE,
#'     type = "lower", p.mat = p.mat)
#' # Leave blank on no significant coefficient
#' ggcorrplot(corr, p.mat = p.mat, hc.order = TRUE,
#'     type = "lower", insig = "blank")
#'
#' @name ggcorrplot
#' @rdname ggcorrplot
#' @export
ggcorrplot <- function (corr, method = c("square", "circle"),
                        type = c("full", "lower", "upper"),
                        ggtheme = ggplot2::theme_minimal,
                        title = "", show.legend = TRUE, legend.title = "Corr", show.diag = FALSE,
                        colors = c("blue", "white", "red"), outline.color = "gray",
                        hc.order = FALSE, hc.method = "complete",
                        lab = FALSE, lab_col = "black", lab_size = 4,
                        p.mat = NULL, sig.level = 0.05, insig = c("pch", "blank"),
                        pch = 4, pch.col = "black", pch.cex = 5,
                        tl.cex = 12, tl.col = "black", tl.srt = 45) {

  type <- match.arg(type)
  method <- match.arg(method)
  insig <- match.arg(insig)

  if (!is.matrix(corr) & !is.data.frame(corr))
    stop("Need a matrix or data frame!")
  corr <- as.matrix(corr)

  if (hc.order) {
    ord <- .hc_cormat_order(corr)
    corr <- corr[ord, ord]
    if (!is.null(p.mat))
      p.mat <- p.mat[ord, ord]
  }

  # Get lower or upper triangle
  if (type == "lower") {
    corr <- .get_lower_tri(corr, show.diag)
    p.mat <- .get_lower_tri(p.mat, show.diag)
  }else if (type == "upper") {
    corr <- .get_upper_tri(corr, show.diag)
    p.mat <- .get_upper_tri(p.mat, show.diag)
  }
  # Melt corr and pmat
  corr2 <- reshape2::melt(corr, na.rm = TRUE)
  colnames(corr2) <- c("Var1", "Var2", "value")
  corr2$pvalue <- rep(NA, nrow(corr2))
  corr2$signif <- rep(NA, nrow(corr2))
  if (!is.null(p.mat)) {
    p.mat <- reshape2::melt(p.mat, na.rm = TRUE)
    corr2$coef <- corr2$value
    corr2$pvalue <- p.mat$value
    corr2$signif <-  as.numeric(p.mat$value <= sig.level)
    p.mat <- subset(p.mat, p.mat$value > sig.level)
    if (insig == "blank")
      corr2$value <- corr2$value * corr2$signif
  }

  corr2$abs_corr <- abs(corr2$value) * 10

  # Heatmap
  p <-
    ggplot2::ggplot(corr2, ggplot2::aes_string("Var1", "Var2", fill = "value"))
  if (method == "square") {p <- p + ggplot2::geom_tile(color = outline.color)
  }else if (method == "circle") {
    p <- p + ggplot2::geom_point(color = outline.color,
                                 shape = 21, ggplot2::aes_string(size = "abs_corr")) +
      ggplot2::scale_size(range = c(4, 10)) + ggplot2::guides(size = FALSE)
  }

  p <-
    p + ggplot2::scale_fill_gradient2(
      low = colors[1], high = colors[3], mid = colors[2],
      midpoint = 0, limit = c(-1,1), space = "Lab",
      name = legend.title
    ) +
    ggtheme() +
    ggplot2::theme(
      axis.text.x = ggplot2::element_text(
        angle = tl.srt, vjust = 1, size = tl.cex, hjust = 1
      ),
      axis.text.y = ggplot2::element_text(size = tl.cex)
    ) +
    ggplot2::coord_fixed()

  label <- round(corr[, "value"], 2)
  if (lab)
    p <- p +
    ggplot2::geom_text(
      ggplot2::aes_string("Var1", "Var2"),
      label = label,
      color = lab_col, size = lab_size
    )

  if (!is.null(p.mat) & insig == "pch") {
    p <- p + ggplot2::geom_point(
      data = p.mat,
      ggplot2::aes_string("Var1", "Var2"),
      shape = pch, size = pch.cex, color = pch.col
    )
  }

  # Add titles
  if (title != "")
    p <- p + ggplot2::ggtitle(title)
  if (!show.legend)
    p <- p + ggplot2::theme(legend.position = "none")


  p <- p + .no_panel()
  p
}



#' Compute the matrix of correlation p-values
#'
#' @param x numeric matrix or data frame
#' @param ... other arguments to be passed to the function cor.test.
#' @rdname ggcorrplot
#' @export
cor_pmat <- function(x, ...) {
  mat <- as.matrix(x)
  n <- ncol(mat)
  p.mat <- matrix(NA, n, n)
  diag(p.mat) <- 0
  for (i in 1:(n - 1)) {
    for (j in (i + 1):n) {
      tmp <- stats::cor.test(mat[, i], mat[, j], ...)
      p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
    }
  }
  colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
  p.mat
}




#+++++++++++++++++++++++
# Helper Functions
#+++++++++++++++++++++++

# Get lower triangle of the correlation matrix
.get_lower_tri <- function(cormat, show.diag = FALSE) {
  if (is.null(cormat))
    return(cormat)
  cormat[upper.tri(cormat)] <- NA
  if (!show.diag)
    diag(cormat) <- NA
  return(cormat)
}

# Get upper triangle of the correlation matrix
.get_upper_tri <- function(cormat, show.diag = FALSE) {
  if (is.null(cormat))
    return(cormat)
  cormat[lower.tri(cormat)] <- NA
  if (!show.diag)
    diag(cormat) <- NA
  return(cormat)
}

# hc.order correlation matrix
.hc_cormat_order <- function(cormat, hc.method = "complete") {
  dd <- stats::as.dist((1 - cormat) / 2)
  hc <- stats::hclust(dd, method = hc.method)
  hc$order
}

.no_panel <- function() {
  ggplot2::theme(
    axis.title.x = ggplot2::element_blank(),
    axis.title.y = ggplot2::element_blank()
  )
}