plot bivariate normal distribution in R

MVNormPlot.R

# need mvtnorm package
library("mvtnorm")
range = seq(-5,5,0.1)
mean = c(0,0)
Sigma = matrix(c(1, .5, .5, 1), 2)
out = matrix (rep(0,101*101),101)

for (i in 1:length(range)){
	for (j in 1:length(range)){
		out[i,j] = dmvnorm(c(range[i],range[j]),mean=mean,sigma=Sigma)
	}
}
persp(out,theta = 30,phi = 30,col="lightblue")

Normate Bivariate Kernel Estimate.R

library(MASS)
bivn <- mvrnorm(100000, mu = c(0, 0), Sigma = matrix(c(1, .5, .5, 1), 2))

# now we do a kernel density estimate
bivn.kde <- kde2d(bivn[,1], bivn[,2], n = 50)

persp(bivn.kde, phi = 45, theta = 30)