#' Plots a matrix as an image #' #' @param A a matrix to be plotted #' @param add.values boolean indicating if matrix values are to be written into #' matrix element boxes #' @param main overall title for the plot #' @param sub sub-title of the plot #' @param interpolate a logical vector (or scalar) indicating whether to apply #' linear interpolation to the image when drawing. #' @param ... further arguments passed to \code{\link{rasterImage}} #' #' @examples #' AR <- 0.5^abs(outer(1:10, 1:10, `-`)) #' matrixImage(AR, AR > 0.2, main = "Autoregressiv Covariance") #' #' @export matrixImage <- function(A, add.values = FALSE, main = NULL, sub = NULL, interpolate = FALSE, ..., zlim = NA, axes = TRUE, asp = 1, col = hcl.colors(24, "YlOrRd", rev = FALSE), digits = getOption("digits") ) { # plot raster image plot(c(0, ncol(A)), c(0, nrow(A)), type = "n", bty = "n", col = "black", xlab = "", ylab = "", xaxt = "n", yaxt = "n", main = main, sub = sub, asp = asp) # Scale values of `A` to [0, 1] with min mapped to 1 and max to 0. if (missing(zlim)) { S <- (max(A) - A) / diff(range(A)) } else { S <- pmin(pmax(0, (zlim[2] - A) / diff(zlim)), 1) } # and not transform to color S <- matrix(col[round((length(col) - 1) * S + 1)], nrow = nrow(A)) # Raster Image ploting the matrix with element values mapped to grayscale # as big elements (original matrix A) are dark and small (negative) elements # are white. rasterImage(S, 0, 0, ncol(A), nrow(A), interpolate = interpolate, ...) # X/Y axes index (matches coordinates to matrix indices) x <- seq(1, ncol(A), by = 1) y <- seq(1, nrow(A)) if (axes) { axis(1, at = x - 0.5, labels = x, lwd = 0, lwd.ticks = 1) axis(2, at = y - 0.5, labels = rev(y), lwd = 0, lwd.ticks = 1, las = 1) } # Writes matrix values if (any(add.values)) { if (length(add.values) > 1) { A[!add.values] <- NA A[add.values] <- format(A[add.values], digits = digits) } text(rep(x - 0.5, each = nrow(A)), rep(rev(y - 0.5), ncol(A)), A, adj = 0.5) } }