Surrogate (version 3.3.3)

fit_copula_ContCont: Fit continuous-continuous vine copula model

Description

fit_copula_ContCont() fits the continuous-continuous vine copula model. See Details for more information about this model.

Usage

fit_copula_ContCont(
  data,
  copula_family,
  marginal_S0,
  marginal_S1,
  marginal_T0,
  marginal_T1,
  start_copula,
  method = "BFGS",
  ...
)

Value

Returns an S3 object that can be used to perform the sensitivity analysis with sensitivity_analysis_copula().

Arguments

data

data frame with three columns in the following order: surrogate endpoint, true endpoint, and treatment indicator (0/1 coding). Ordinal endpoints should be integers starting from 1.

copula_family

One of the following parametric copula families: "clayton", "frank", "gaussian", or "gumbel". The first element in copula_family corresponds to the control group, the second to the experimental group.

marginal_S0, marginal_S1, marginal_T0, marginal_T1

List with the following three elements (in order):

• Density function with first argument x and second argument para the parameter vector for this distribution.

• Distribution function with first argument x and second argument para the parameter vector for this distribution.

• Inverse distribution function with first argument p and second argument para the parameter vector for this distribution.

• The number of elements in para.

• A vector of starting values for para.

start_copula

Starting value for the copula parameter.

method

Optimization algorithm for maximizing the objective function. For all options, see ?maxLik::maxLik. Defaults to "BFGS".

...

Extra argument to pass onto maxLik::maxLik

Author

Florian Stijven

See Also

sensitivity_analysis_copula(), print.vine_copula_fit(), plot.vine_copula_fit()