compute_ICA_BinCont {Surrogate} | R Documentation |
Compute Individual Causal Association for a given D-vine copula model in the Binary-Continuous Setting
Description
The compute_ICA_BinCont()
function computes the individual causal
association for a fully identified D-vine copula model in the setting with a
continuous surrogate endpoint and a binary true endpoint.
Usage
compute_ICA_BinCont(
copula_par,
rotation_par,
copula_family1,
copula_family2 = copula_family1,
n_prec,
q_S0,
q_S1,
marginal_sp_rho = TRUE,
seed = 1
)
Arguments
copula_par |
Parameter vector for the sequence of bivariate copulas that
define the D-vine copula. The elements of |
rotation_par |
Vector of rotation parameters for the sequence of
bivariate copulas that define the D-vine copula. The elements of
|
copula_family1 |
Copula family of |
copula_family2 |
Copula family of the other bivariate copulas. For the
possible options, see |
n_prec |
Number of Monte Carlo samples for the computation of the mutual information. |
q_S0 |
Quantile function for the distribution of |
q_S1 |
Quantile function for the distribution of |
marginal_sp_rho |
(boolean) Compute the sample Spearman correlation
matrix? Defaults to |
seed |
Seed for Monte Carlo sampling. This seed does not affect the global environment. |
Value
(numeric) A Named vector with the following elements:
ICA
Spearman's rho,
\rho_s (\Delta S, \Delta T)
(if asked)Kendall's tau,
\tau (\Delta S, \Delta T)
(if asked)Marginal association parameters in terms of Spearman's rho:
(\rho_s(S_0, S_1), \rho_s(S_0, T_0), \rho_s(S_0, T_1), \rho_s(S_1, T_0), \rho_s(S_0, S_1), \rho_s(T_0, T_1)