Created
March 27, 2016 21:36
How to fit a copula model in R [heavily revised]. Part 2: fitting the copula. Full article at
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# Build the bivariate distribution | |
my_dist <- mvdc(claytonCopula(param = 1.48, dim = 2), margins = c("gamma","gamma"), paramMargins = list(list(shape = x_shape, rate = x_rate), list(shape = y_shape, rate = y_rate))) | |
# Generate random sample observations from the multivariate distribution | |
v <- rMvdc(5000, my_dist) | |
# Compute the density | |
pdf_mvd <- dMvdc(v, my_dist) | |
# Compute the CDF | |
cdf_mvd <- pMvdc(v, my_dist) | |
# 3D plain scatterplot of the generated bivariate distribution | |
par(mfrow = c(1, 2)) | |
scatterplot3d(v[,1],v[,2], pdf_mvd, color="red", main="Density", xlab = "u1", ylab="u2", zlab="pMvdc",pch=".") | |
scatterplot3d(v[,1],v[,2], cdf_mvd, color="red", main="CDF", xlab = "u1", ylab="u2", zlab="pMvdc",pch=".") | |
persp(my_dist, dMvdc, xlim = c(-4, 4), ylim=c(0, 2), main = "Density") | |
contour(my_dist, dMvdc, xlim = c(-4, 4), ylim=c(0, 2), main = "Contour plot") | |
persp(my_dist, pMvdc, xlim = c(-4, 4), ylim=c(0, 2), main = "CDF") | |
contour(my_dist, pMvdc, xlim = c(-4, 4), ylim=c(0, 2), main = "Contour plot") |
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