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# ---------------------------------------
# Author: Andreas Alfons, Bernd Prantner
# Vienna University of Technology
# ---------------------------------------
#' Scatterplot with information about missing/imputed values
#'
#' In addition to a standard scatterplot, lines are plotted for the missing
#' values in one variable. If there are imputed values, they will be
#' highlighted.
#'
#' Information about missing values in one variable is included as vertical or
#' horizontal lines, as determined by the `side` argument. The lines are
#' thereby drawn at the observed x- or y-value. In case of imputed values, they
#' will additionally be highlighted in the scatterplot. Supplementary,
#' percentage coverage ellipses can be drawn to give a clue about the shape of
#' the bivariate data distribution.
#'
#' If `interactive`is `TRUE`, clicking in the bottom margin redraws
#' the plot with information about missing/imputed values in the first variable
#' and clicking in the left margin redraws the plot with information about
#' missing/imputed values in the second variable. Clicking anywhere else in
#' the plot quits the interactive session.
#'
#' @param x a `matrix` or `data.frame` with two columns.
#' @param delimiter a character-vector to distinguish between variables and
#' imputation-indices for imputed variables (therefore, `x` needs to have
#' [colnames()]). If given, it is used to determine the corresponding
#' imputation-index for any imputed variable (a logical-vector indicating which
#' values of the variable have been imputed). If such imputation-indices are
#' found, they are used for highlighting and the colors are adjusted according
#' to the given colors for imputed variables (see `col`).
#' @param side if `side=1`, a rug representation and vertical lines are
#' plotted for the missing/imputed values in the second variable; if
#' `side=2`, a rug representation and horizontal lines for the
#' missing/imputed values in the first variable.
#' @param col a vector of length four giving the colors to be used in the plot.
#' The first color is used for the scatterplot, the second/third color for the
#' rug representation for missing/imputed values. The second color is also used
#' for the lines for missing values. Imputed values will be highlighted with
#' the third color, and the fourth color is used for the ellipses (see
#' \sQuote{Details}). If only one color is supplied, it is used for the
#' scatterplot, the rug representation and the lines, whereas the default color
#' is used for the ellipses. Else if a vector of length two is supplied, the
#' default color is used for the ellipses as well.
#' @param alpha a numeric value between 0 and 1 giving the level of
#' transparency of the colors, or `NULL`. This can be used to prevent
#' overplotting.
#' @param lty a vector of length two giving the line types for the lines and
#' ellipses. If a single value is supplied, it will be used for both.
#' @param lwd a vector of length two giving the line widths for the lines and
#' ellipses. If a single value is supplied, it will be used for both.
#' @param quantiles a vector giving the quantiles of the chi-square
#' distribution to be used for the tolerance ellipses, or `NULL` to
#' suppress plotting ellipses (see \sQuote{Details}).
#' @param inEllipse plot lines only inside the largest ellipse. Ignored if
#' `quantiles` is `NULL` or if there are imputed values.
#' @param zeros a logical vector of length two indicating whether the variables
#' are semi-continuous, i.e., contain a considerable amount of zeros. If
#' `TRUE`, only the non-zero observations are used for computing the
#' tolerance ellipses. If a single logical is supplied, it is recycled.
#' Ignored if `quantiles` is `NULL`.
#' @param xlim,ylim axis limits.
#' @param main,sub main and sub title.
#' @param xlab,ylab axis labels.
#' @param interactive a logical indicating whether the `side` argument can
#' be changed interactively (see \sQuote{Details}).
#' @param \dots further graphical parameters to be passed down (see
#' [graphics::par()]).
#' @note The argument `zeros` has been introduced in version 1.4. As a
#' result, some of the argument positions have changed.
#' @author Andreas Alfons, modifications by Bernd Prantner
#' @seealso [marginplot()]
#' @references M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete
#' data using visualization tools. *Journal of Advances in Data Analysis
#' and Classification*, Online first. DOI: 10.1007/s11634-011-0102-y.
#' @keywords hplot
#' @family plotting functions
#' @examples
#'
#' data(tao, package = "VIM")
#' ## for missing values
#' scattMiss(tao[,c("Air.Temp", "Humidity")])
#'
#' ## for imputed values
#' scattMiss(kNN(tao[,c("Air.Temp", "Humidity")]), delimiter = "_imp")
#'
#' @export scattMiss
scattMiss <- function(x, delimiter = NULL, side = 1, col = c("skyblue","red","orange","lightgrey"),
alpha = NULL, lty = c("dashed","dotted"),
lwd = par("lwd"), quantiles = c(0.5, 0.975),
inEllipse = FALSE, zeros = FALSE,
xlim = NULL, ylim = NULL, main = NULL,
sub = NULL, xlab = NULL, ylab = NULL,
interactive = TRUE, ...) {
# error messages
if(!(inherits(x, c("data.frame","matrix")))) {
stop("'x' must be a data.frame or matrix")
}
imputed <- FALSE # indicates if there are Variables with missing-index
## delimiter ##
if(!is.null(delimiter)) {
tmp <- grep(delimiter, colnames(x)) # Position of the missing-index
if(length(tmp) > 0) {
imp_var <- x[, tmp, drop=FALSE]
x <- x[, -tmp, drop=FALSE]
if(ncol(x) == 0) stop("Only the missing-index is given")
if(is.matrix(imp_var) && range(imp_var) == c(0,1)) imp_var <- apply(imp_var,2,as.logical)
if(is.null(dim(imp_var))) {
if(!is.logical(imp_var)) stop("The missing-index of imputed Variables must be of the type logical")
} else {
if(!any(as.logical(lapply(imp_var,is.logical)))) stop("The missing-index of imputed Variables must be of the type logical")
}
imputed <- TRUE
} else {
warning("'delimiter' is given, but no missing-index-Variable is found", call. = FALSE)
}
}
if(ncol(x) != 2) stop("'x' must be 2-dimensional")
if(length(col) == 0) col <- c("skyblue","red","orange","lightgrey")
else if(length(col) == 1) col <- c(rep.int(col, 3), "lightgrey")
else if(length(col) == 2) col <- c(rep(col,1:2), "lightgrey")
else if(length(col) == 3) col <- c(col,"lightgrey")
else if(length(col) != 4) stop("'col' must be a vector of length 3 or 4")
if(length(side) == 0) s <- 1
else {
s <- side[1]
if(!(s %in% 1:2)) stop("'side' must be 1 or 2")
}
if(length(lty) == 0) lty <- c("dashed","dotted")
else if(length(lty) == 1) lty <- rep.int(lty, 2)
else if(length(lty) > 2) lty <- lty[1:2]
if(length(lwd) == 0) lwd <- rep.int(par("lwd"), 2)
else if(length(lwd) == 1) lwd <- rep.int(lwd, 2)
else if(length(lwd) > 2) lwd <- lwd[1:2]
if(!is.logical(zeros) || length(zeros) == 0) zeros <- FALSE
zeros <- rep(sapply(zeros, isTRUE), length.out=2)
# prepare data
if(is.data.frame(x)) x <- data.matrix(x)
else if(mode(x) != "numeric") mode(x) <- "numeric"
iInf <- apply(x, 1, function(x) any(is.infinite(x)))
if(any(iInf)) {
x <- x[!iInf, , drop=FALSE]
warning("'x' contains infinite values")
}
# default axis labels
if(is.null(colnames(x))) {
if(is.null(xlab)) xlab <- ""
if(is.null(ylab)) ylab <- ""
}
# semitransparent colors
if(!is.null(alpha)) col <- alphablend(col, alpha)
# count missings
n <- nrow(x)
if(!imputed) {
nNA <- apply(x, 2, countNA)
} else {
nNA <- countImp(x, delimiter, imp_var)
}
plot.ellipse <- !is.null(quantiles) && all(n - nNA > 2)
createPlot <- function() {
if(is.null(xlim) && nNA[1] == n) xlim <- rep.int(0, 2)
if(is.null(ylim) && nNA[2] == n) ylim <- rep.int(0, 2)
sm <- s %% 2 + 1
if(nNA[sm]) { # missings in other variable
if(plot.ellipse) {
# define ellipse
xMcd <- x
if(zeros[1]) xMcd <- xMcd[xMcd[,1] != 0,]
if(zeros[2]) xMcd <- xMcd[xMcd[,2] != 0,]
cov <- covMcd(xMcd, alpha=0.75)
cen <- cov$cen
cov <- cov$cov
cov.svd <- svd(cov, nv=0)
r <- cov.svd[["u"]] %*% diag(sqrt(cov.svd[["d"]]))
t <- 2*pi*(0:100)/100 # parameter for ellipse
qch <- qchisq(quantiles, 2) # quantiles of qchisquare dist.
getEll <- function(q, t, r, cen) {
e <- cbind(cos(t) * sqrt(q), sin(t) * sqrt(q))
t(r %*% t(e)) + rep(1, 101) %o% cen
}
tt <- sapply(qch, getEll, t, r, cen, simplify=FALSE)
ttmax <- tt[[which.max(quantiles)]]
# axis limits
if(is.null(xlim)) {
xlim <- range(c(x[,1], ttmax[,1]), na.rm=TRUE)
}
if(is.null(ylim)) {
ylim <- range(c(x[,2], ttmax[,2]), na.rm=TRUE)
}
}
if(!imputed) miss <- is.na(x)
else {
tmp <- isImp(x, pos = 1, delimiter = delimiter, imp_var = imp_var, selection = "none")
miss <- cbind(tmp[["misspos"]],tmp[["missh"]])
}
xsmiss <- x[!miss[,s] & miss[,sm], s]
localPlot <- function(..., type, plot.first, plot.last) {
plot(...,
panel.first={
if(plot.ellipse) # plot ellipses
for(t in tt) lines(t[,1], t[,2], col=col[4],
lty=lty[2], lwd=lwd[2])
# plot points instead of lines when imputed values exist
if(!imputed) {
if(plot.ellipse && inEllipse) {
xsell <- xsmiss[min(ttmax[,s]) <= xsmiss &
xsmiss <= max(ttmax[,s])]
if(length(xsell)) {
rho <- cov[1,2]/sqrt(cov[1,1]*cov[2,2])
phalf <- rho*sqrt(cov[sm,sm])*
(xsell-cen[s])/sqrt(cov[s,s])
q <- cov[sm,sm]*((xsell-cen[s])^2/cov[s,s]-
(1-rho^2)*qch[length(qch)])
z <- rbind(phalf + sqrt(phalf^2-q),
phalf - sqrt(phalf^2-q))
xsmell <- z + cen[sm]
xsell <- as.vector(rbind(xsell, xsell,
rep(NA, length(xsell))))
xsmell <- as.vector(rbind(xsmell,
rep(NA, ncol(xsmell))))
if(s == 1) lines(xsell, xsmell, col=col[2],
lty=lty[1], lwd=lwd[1])
else lines(xsmell, xsell, col=col[2],
lty=lty[1], lwd=lwd[1])
}
} else {
if(s == 1) {
abline(v=xsmiss, col=col[2],
lty=lty[1], lwd=lwd[1])
} else {
abline(h=xsmiss, col=col[2],
lty=lty[1], lwd=lwd[1])
}
}
}
})
}
} else {
localPlot <- function(..., type, plot.first, plot.last) plot(...)
rugNA(x[,1], x[, 2], miss = miss, side=s, col=ifelse(!imputed,col[2],col[3]))
}
localPlot(x, col=col[1], xlim=xlim, ylim=ylim,
main=main, sub=sub, xlab=xlab, ylab=ylab, ...)
# plot points instead of lines when imputed values exist
if(!imputed) miss <- NULL
else {
points(x[miss[,sm],], col=col[3])
# fix bug - in case there are still some NA after imputation
indices <- which(!is.na(x[miss[,sm],2]))
if(s == 1) {
abline(v=xsmiss[-indices], col=col[2],
lty=lty[1], lwd=lwd[1])
} else {
abline(h=xsmiss[-indices], col=col[2],
lty=lty[1], lwd=lwd[1])
}
indices1 <- which(miss[,2]==TRUE & is.na(x[,2]))
indices2 <- which(miss[,2]==TRUE & !is.na(x[,2]))
miss1 <- miss2 <- miss
miss1[indices1,2] <- FALSE
miss2[indices2,2] <- FALSE
rugNA(x[, 1], x[, 2], miss = miss1, side=s, col=col[3])
rugNA(x[, 1], x[, 2], miss = miss2, side=s, col=col[2])
}
#####
#rugNA(x[,1], x[, 2], miss = miss, side=s, col=ifelse(!imputed,col[2],col[3]))
}
createPlot()
dev <- names(dev.cur())
interactiveDevices <- c("X11","quartz","windows")
if(interactive && any(!is.na(charmatch(interactiveDevices, dev)))) {
cat(paste("\nClick in bottom or left margin to",
"change the 'side' argument accordingly.\n"))
cat(paste("To regain use of the VIM GUI and the R console,",
"click anywhere else in the graphics window.\n\n"))
usr <- par("usr")
pt <- locatorVIM()
while(!is.null(pt) &&
((usr[1] <= pt$x && pt$x <= usr[2] && pt$y < usr[3]) ||
(pt$x < usr[1] && usr[3] <= pt$y && pt$y <= usr[4]))) {
s <- if(usr[1] <= pt$x && pt$x <= usr[2] && pt$y < usr[3]) 1 else 2
createPlot()
usr <- par("usr")
pt <- locatorVIM()
}
}
invisible()
}
|
