fit_copula_ContCont: Fit continuous-continuous vine copula model

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

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",
  ...
)

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

Value

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

Author(s)

Florian Stijven

See Also

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

Surrogate documentation built on April 11, 2025, 6:09 p.m.