MGL.reg.mixed: Fitting bivariate mixed MGL and MGL-EV copula regression...
In lizhengxiao/rMGLReg: MGL copula regressions

Description Usage Arguments Details Value Examples

MGL.reg.mixed is used to fit bivariate MGL and MGL-EV copula regression models for continuous and semi-continuous variables.

MGL.reg.mixed(
  obs,
  U,
  U_,
  f,
  X,
  copula = c("MGL", "MGL180", "MGL-EV", "MGL-EV180", "Gumbel", "MGB2"),
  umin = 0,
  hessian = TRUE,
  initpar,
  ...
)

`obs`	two-dimensional matrix for observations.
`U`	two-dimensional matrix for pseudo copula data with values in [0,1] for (F(y1), F(y2)).
`U_`	two-dimensional matrix for pseudo copula data for the data (F(y1), F(y2-1)).
`f`	two-dimensional matrix for the density function of marginal distributions.
`X`	design matrix.
`copula`	'MGL', 'MGL180', "MGL-EV", "MGL-EV180"
`umin`	threshold value used in the semi-continuous data.
`hessian`	Logical. Should a numerically differentiated Hessian matrix be returned?
`initpar`	Initial values for the parameters to be optimized over.
`...`	additional arguments, see `optim` for more details.

The estimation method is performed via optim function. Y1 and Y2 are both continuous variables.

Y1: continuous data.
Y2: semi-continuous data where Y2>umin is continuous and Y2<=umin is discrete.

copula: "MGL180" and "MGLEV180" denote the survival MGL and survival MGL-EV copula respectively.

For "Gumbel" regression model, the copula parameter

δ_i = \exp(Xβ) + 1.
For "MGL", "MGL180", "MGL-EV", "MGL-EV180" regression model, the copula parameter

δ_i = \exp(Xβ),

where β is the vector of coefficients to be estimated in the copula regression.

A list containing the following components:

loglike: the value of the estimated maximum of the loglikelihood function.
copula: the name of the fitted copula. "MGL180" and "MGL-EV180" denote the survival MGL and MGL-EV copula respectively.
estimates: the point at which the maximum value of the loglikelihood is obtained.
se: the standard errors of the estimators.
AIC, BIC: the goodness fit of the regression models.
hessian: the hessian at the estimated maximum of the loglikelihood (if requested).

library(rMGLReg)
u <- cbind(earthqCHI$u1, earthqCHI$u2)
u_ <- cbind(earthqCHI$u1, earthqCHI$u2_)
y <- cbind(earthqCHI$y1, earthqCHI$y2)
f <- cbind(earthqCHI$f1, earthqCHI$f2)
obs <- y
U <- u
U_ <- u_
umin <- 20
library(splines)
X <- ns(earthqCHI$year, knots = quantile(earthqCHI$year, c(0.333, 0.667)), intercept = TRUE)
m.MGL180 <- MGL.reg.mixed(obs = y, U = U, U_ = U_, umin = umin, f = f, X = X,
copula = "MGL180",
                         method = "Nelder-Mead",
                        control = list(maxit = 100000),
                        initpar = c(0.64, 1.2, 1, -0.2))

m.MGLEV180 <- MGL.reg.mixed(obs = y, U = U, U_ = U_, umin = umin, f = f, X = X,
                           copula = "MGL-EV180",
                           method = "Nelder-Mead",
                          control = list(maxit = 100000),
                          initpar = c(-0.32, 1, 1, 1))
m.MGL180
m.MGLEV180