我题目也给出了.真的不是很容易.

着急啊.帮忙啊.

library(MASS)
Sigma <- matrix(c(1,0.7,0.7,1),2,2)
Sigma
r=mvrnorm(n=1000, c(0,3), Sigma)
par(mfrow = c(2, 2))
#density plot
plot(density(r))
#kernel density estimate
bivn.kde <- kde2d(r[,1], r[,2], n = 50)
#perspective plot
persp(bivn.kde, phi = 45, theta = 30)
#contour plot
contour(bivn.kde)
#contour plot with image
image(bivn.kde,col = terrain.colors(100)); contour(bivn.kde, add = T)
  

非常感谢.就这点程序我初学者还挺难的.

*
* GCONTOUR.RPF
* RATS Version 8, Introduction, Example from Section 4.13.
*
* Create a series which is mean 0 through entry 50, and mean 10 from 51
* through the end of the sample. We use a SEED to ensure that the graph
* can be reproduced
*
seed 95035
all 100
set x = %if(t>50,10,0)+%ran(1.0)
*
* The sum of squared residuals for a break between B and B+1 and a
* post-break mean of mu (pre-break mean assumed to be zero) is
*
*   sum (all t) of x^2 - 2*mu*sum (t>B) of x + (T-B)*mu^2
*
* This generates a contour plot of the concentrated log likelihood,
* assuming Normal residuals, and fixed, but unknown, variance.
*
* Set up the grids for breaks (1,...,100) and mus (.2,.4,...,20)
*
compute breaks=%seqa(1.0,1.0,100)
compute mus   =%seqa( .2, .2,100)
*
* This generates as "over" the sum of x for elements above t.
*
acc x / ax
set over = ax(100)-ax(t)
*
* Compute the sum of squares
*
compute x2=%normsqr(x)
*
* Generate the log likelihood for all the combinations of mus and breaks.
*
dec rect f(100,100)
ewise f(i,j)=-50.0*log((x2-2*mus(i)*over(fix(breaks(j)))+(100-breaks(j))*mus(i)^2)/100.0)
*
* Do the contour graph with a grid line across the actual break (50)
*
gcontour(x=mus,y=breaks,f=f,vgrid=||1||)

谢谢