In lizhengxiao/rMGLReg: MGL copula regressions

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

$d=2$ without covariates

library(rMGLReg)
set.seed(112)
Usim <- rcMGL.multi(n = 1000, d = 2, pars = 2)
plot(Usim)
m.MGL <- MGL.mle(Usim,
                  copula  = "MGL",
                  initpar = c(2), hessian = TRUE)
# estimation results
m.MGL

$d=10$ without covariates

library(rMGLReg)
set.seed(112)
Usim <- rcMGL.multi(n = 500, d = 10, pars = 2)
round(cor(Usim, method = "kendall"), 2)
m.MGL <- MGL.mle(Usim,
                  copula  = "MGL",
                  initpar = c(2),
                 hessian = TRUE)
# estimation results
m.MGL

$d=2$ with covariates $x_1$ and $x_2$

# simulated the data 
set.seed(111)
n <- 500
beta.true <- c(-0.6, 0.5, 0.2)
d <- 2
x1 <- rnorm(n, 0, 1)
x2 <- rnorm(n, 0, 1)
X <- model.matrix(~ x1 + x2)
delta.sim <- as.vector(exp(X%*%beta.true))
summary(delta.sim)
Usim <- matrix(0, nrow = n, ncol = d)
for (i in 1:n){
  Usim[i, ] <- rcMGL.multi(n = 1, d = d, pars = delta.sim[i])
}
plot(Usim)

m.MGL <- MGL.reg(U = Usim,
                 X = X,
                  copula  = "MGL",
                 hessian = TRUE,
                  initpar = c(-0.6, 0.5, 0.2))
# estimation results
m.MGL

$d=10$ with covariates $x_1$ and $x_2$

# simulated the data 
set.seed(111)
n <- 500
beta.true <- c(-0.6, 0.5, 0.2)
d <- 10
x1 <- rnorm(n, 0, 1)
x2 <- rnorm(n, 0, 1)
X <- model.matrix(~ x1 + x2)
delta.sim <- as.vector(exp(X%*%beta.true))
summary(delta.sim)

Usim <- matrix(0, nrow = n, ncol = d)
for (i in 1:n){
  Usim[i, ] <- rcMGL.multi(n = 1, d = d, pars = delta.sim[i])
}
round(cor(Usim, method = "kendall"), 2)

m.MGL <- MGL.reg(U = Usim,
                 X = X,
                  copula  = "MGL",
                 hessian = TRUE,
                  initpar = c(-0.6, 0.5, 0.2))
# estimation results
m.MGL