50 lines
1.8 KiB
R
50 lines
1.8 KiB
R
#' Plots a matrix as an image
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#'
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#' @param A a matrix to be plotted
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#' @param add.values boolean indicating if matrix values are to be written into
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#' matrix element boxes
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#' @param main overall title for the plot
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#' @param sub sub-title of the plot
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#' @param interpolate a logical vector (or scalar) indicating whether to apply
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#' linear interpolation to the image when drawing.
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#' @param ... further arguments passed to \code{\link{rasterImage}}
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#'
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#' @examples
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#' AR <- 0.5^abs(outer(1:10, 1:10, `-`))
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#' matrixImage(AR, AR > 0.2, main = "Autoregressiv Covariance")
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#'
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#' @export
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matrixImage <- function(A, add.values = FALSE,
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main = NULL, sub = NULL, interpolate = FALSE, ...,
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digits = getOption("digits")
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) {
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# plot raster image
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plot(c(0, ncol(A)), c(0, nrow(A)), type = "n", bty = "n", col = "red",
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xlab = "", ylab = "", xaxt = "n", yaxt = "n", main = main, sub = sub)
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# Scale values of `A` to [0, 1] with min mapped to 1 and max to 0.
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S <- (max(A) - A) / diff(range(A))
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# Raster Image ploting the matrix with element values mapped to grayscale
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# as big elements (original matrix A) are dark and small (negative) elements
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# are white.
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rasterImage(S, 0, 0, ncol(A), nrow(A), interpolate = interpolate, ...)
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# Add X-axis giving index
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x <- seq(1, ncol(A), by = 1)
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axis(1, at = x - 0.5, labels = x, lwd = 0, lwd.ticks = 1)
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# Add Y-axis
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y <- seq(1, nrow(A))
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axis(2, at = y - 0.5, labels = rev(y), lwd = 0, lwd.ticks = 1, las = 1)
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# Writes matrix values (in colored element grids)
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if (any(add.values)) {
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if (length(add.values) > 1) {
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A[!add.values] <- NA
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A[add.values] <- format(A[add.values], digits = digits)
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}
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text(rep(x - 0.5, each = nrow(A)), rep(rev(y - 0.5), ncol(A)), A,
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adj = 0.5, col = as.integer(S > 0.65))
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}
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}
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