Nothing
setMethod("plotKML", "SpatialPredictions", function(
obj,
folder.name = normalizeFilename(deparse(substitute(obj, env=parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour,
grid2poly = FALSE,
obj.summary = FALSE,
plot.svar = FALSE,
pngwidth = 210,
pngheight = 580,
pngpointsize = 14,
metadata = NULL,
kmz = get("kmz", envir = plotKML.opts),
open.kml = TRUE,
...
){
## Guess aesthetics if missing:
varname <- paste(obj@variable)
if(missing(colour)){
obj@predicted@data[,"colour"] <- obj@predicted@data[,varname]
} else {
if(is.name(colour)|is.call(colour)){
obj@predicted@data[,"colour"] <- eval(colour, obj@predicted@data)
} else {
obj@predicted@data[,"colour"] <- obj@predicted@data[,as.character(colour)]
}
}
pred <- obj@predicted["colour"]
## sampling locations:
locs <- obj@observed
labs <- paste(signif(locs@data[,varname], 3))
## summary properties of the RK model:
if(obj.summary==TRUE){
xx <- summary(obj)
xd <- unlist(xx[!names(xx) %in% c("bonds", "breaks")])
md <- data.frame(Names=attr(xd, "names"), Values=xd, stringsAsFactors = FALSE)
html <- kml_description(md, asText = TRUE, cwidth = 120, twidth = 240)
}
if(grid2poly == TRUE){
pol <- grid2poly(pred)
}
kml_open(folder.name = folder.name, file.name = file.name)
if(obj.summary==TRUE){
## add a description for the whole folder:
kml.out <- get("kml.out", envir=plotKML.fileIO)
description_txt <- sprintf('<description><![CDATA[%s]]></description>', html)
parseXMLAndAdd(description_txt, parent=kml.out[["Document"]])
assign('kml.out', kml.out, envir=plotKML.fileIO)
}
if(grid2poly == TRUE){
kml_layer(pol, colour = colour, ...)
} else {
kml_layer(pred, colour = colour, raster_name = paste(varname, "_predicted.png", sep=""), metadata = metadata, ...)
}
if(plot.svar==TRUE){
## plot the prediction variance?
svarname <- paste(obj@variable, ".", "svar", sep="")
svar <- obj@predicted[svarname]
names(svar) <- "colour"
kml_layer(svar, colour = colour, colour_scale = get("colour_scale_svar", envir = plotKML.opts), raster_name = paste(svarname, "_svar.png", sep=""), plot.legend = FALSE)
}
kml_layer(obj = locs, points_names = labs)
## plot the correlation graph and variogram:
if(any(names(obj@validation) %in% "var1.pred")){
if(all(!is.na(obj@validation$var1.pred)) & all(!is.na(obj@validation$observed))){
png(filename=paste(varname, "_gstatplots.png", sep=""), width=pngwidth, height=pngheight, bg="white", pointsize=pngpointsize)
plot.SpatialPredictions(obj, plot.predictions=FALSE, vertical=TRUE)
dev.off()
## add the SpatialPredictions plot:
kml_screen(image.file = paste(varname, "_gstatplots.png", sep=""), position = "LL", sname = "gstatModel summary plot")
}
}
## close the file:
kml_close(file.name = file.name)
if (kmz == TRUE){
kml_compress(file.name = file.name)
}
## open KML file in the default browser:
if(open.kml==TRUE){
kml_View(file.name)
} else {
message(paste("Object written to:", file.name))
}
})
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