54 lines
1.7 KiB
R
54 lines
1.7 KiB
R
#' Loss distribution elbow plot.
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#'
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#' Boxplots of the loss from \code{min.dim} to \code{max.dim} \code{k} values.
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#'
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#' @param x Object of class \code{"cve"} (result of [\code{\link{cve}}]).
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#' @param ... Pass through parameters to [\code{\link{plot}}] and
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#' [\code{\link{lines}}]
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#' @examples
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#' # create B for simulation
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#' B <- cbind(rep(1, 6), (-1)^seq(6)) / sqrt(6)
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#'
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#' set.seed(21)
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#' # creat predictor data x ~ N(0, I_p)
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#' X <- matrix(rnorm(600), 100)
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#'
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#' # simulate response variable
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#' # y = f(B'x) + err
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#' # with f(x1, x2) = x1^2 + 2 x2 and err ~ N(0, 0.25^2)
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#' Y <- (X %*% B[, 1])^2 + 2 * X %*% B[, 2] + rnorm(100, 0, .1)
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#'
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#' # Create bandwidth estimation function
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#' estimate.bandwidth <- function(X, k, nObs) {
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#' n <- nrow(X)
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#' p <- ncol(X)
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#' X_c <- scale(X, center = TRUE, scale = FALSE)
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#' 2 * qchisq((nObs - 1) / (n - 1), k) * sum(X_c^2) / (n * p)
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#' }
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#' # calculate cve with method 'simple' for k = min.dim,...,max.dim
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#' cve.obj.simple <- cve(Y ~ X, h = estimate.bandwidth, nObs = sqrt(nrow(X)))
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#'
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#' # elbow plot
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#' plot(cve.obj.simple)
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#'
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#' @seealso see \code{\link{par}} for graphical parameters to pass through
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#' as well as \code{\link{plot}}, the standard plot utility.
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#' @method plot cve
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#' @importFrom graphics plot lines points boxplot
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#' @export
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plot.cve <- function(x, ...) {
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L <- c()
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k <- c()
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for (dr.k in x$res) {
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if (class(dr.k) == 'cve.k') {
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k <- c(k, as.character(dr.k$k))
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L <- c(L, dr.k$L)
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}
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}
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L <- matrix(L, ncol = length(k)) / var(x$Y)
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boxplot(L, main = "elbow plot",
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xlab = "SDR dimension",
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ylab = "Sample loss distribution",
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names = k)
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}
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