plotkmc2D {kmc}

Plot the contour plot of log-likelihood around the H0 (dim=2).

Description

Given a kmc object, this function will produce contour plot if there were two constraints.

Usage

plotkmc2D(resultkmc, flist=list(f1=function(x){x}, f2=function(x){x^2}), 
          range0=c(0.2, 3,20))

Arguments

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.

Value

X

x.grid

Y

y.grid

Z

grid value

Author(s)

Yifan Yang([e-mail])

Examples

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")