plotkmc2D | R Documentation |
Given a kmc object, this function will produce contour plot if there were two constraints.
plotkmc2D(resultkmc, flist=list(f1=function(x){x}, f2=function(x){x^2}), range0=c(0.2, 3,20))
resultkmc |
S3 Object of kmcS3. |
flist |
list of two functions,flist=list( f1=function( x ) x ,f2=function( x ) x^2 ) |
range0 |
A vector that helps to determine the range of the contour plot, i.e (center[1]-range0[1], center[2]-range0[2]) to (center+range0[1], center[2]+range0[2]). The third parameter defines the number of grids would be used. |
X |
x.grid |
Y |
y.grid |
Z |
grid value |
Yifan Yang(yfyang.86@hotmail.com)
x <- c( 1, 1.5, 2, 3, 4.2, 5.0, 6.1, 5.3, 4.5, 0.9, 2.1, 4.3) d <- c( 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1) f<-function( x) { x-3.7} myfun5 <- function( x) { x^2-16.5 } # construnct g as a LIST! g=list( f1=f,f2=myfun5) ; kmc.solve( x,d,g) ->re0; #plotkmc2D(re0) ->ZZ; # run this to generate contour plot #Advanced PLOT option using ggplot2: not run #library(reshape2) #volcano3d <- melt(ZZ$Z) #names(volcano3d) <- c("x", "y", "z") #volcano3d$x <- ZZ$X[volcano3d$x]; #volcano3d$y <- ZZ$Y[volcano3d$y]; #### Plot: use ggplot2 #### #library(ggplot2) # v <- ggplot(volcano3d, aes(x, y, z=z)); # v + geom_tile(aes(fill = z)) + # stat_contour()+ # scale_fill_gradientn("Custom Colours",colours=grey.colors(10)); #### Plot: use qplot #### #qplot(x, y, z = z, data = volcano3d, stat = "contour", geom = "path")
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