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, ...)bubbleMiss(...)
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 correspo"any" 
        (highlighting of missing/imputed values in any of the additional 
        variables) and pos 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