growdotMiss(x, coords, map, pos = 1, delimiter = NULL,
selection = c("any", "all"), log = FALSE, col = c("skyblue", "red",
"skyblue4", "red4", "orange", "orange4"), border = par("bg"),
alpha = NULL, scale = NULL, size = NULL, exp = c(0, 0.95, 0.05),
col.map = grey(0.5), legend = TRUE, legtitle = "Legend",
cex.legtitle = par("cex"), cex.legtext = par("cex"), ncircles = 6,
ndigits = 1, interactive = TRUE, ...)data.frame.data.frame with two columns giving the
spatial coordinates of the observations.bgmap.x needs to have
colnames). If given, it is used to determine the corresponding
i"any"
(highlighting of missing/imputed values in any of the additional
variables) and "all" (highlighpos
should be log-transformed.NA to omit borders.NULL. This can be used to prevent
overplotting.format).growdotMiss, further arguments and graphical
parameters to be passed to bgmap. For bubbleMiss, the
arguments to be passed to growdotMiss.exp
defining the shape of the exponential function. Missings/imputed missings
in the variable of interest will be drawn as rectangles.
If interactive=TRUE, detailed information for an observation can be
printed on the console by clicking on the corresponding point. Clicking in
a region that does not contain any points quits the interactive session.bgmap, mapMiss,
colormapMissdata(chorizonDL, package = "VIM")
data(kola.background, package = "VIM")
coo <- chorizonDL[, c("XCOO", "YCOO")]
## for missing values
x <- chorizonDL[, c("Ca","As", "Bi")]
growdotMiss(x, coo, kola.background, border = "white")
## for imputed values
x_imp <- kNN(chorizonDL[,c("Ca","As","Bi" )])
growdotMiss(x_imp, coo, kola.background, delimiter = "_imp", border = "white")Run the code above in your browser using DataLab