delta_method_log_mutinfo() computes the variance of the estimated log
mutual information, given the unidentifiable parameters.
delta_method_log_mutinfo(
fitted_model,
copula_par_unid,
copula_family2,
rotation_par_unid,
n_prec,
mutinfo_estimator = NULL,
composite,
seed,
eps = 0.001
)(numeric) Variance for the estimated ICA based on the delta method, holding the unidentifiable parameters fixed at the user supplied values.
Returned value from fit_model_SurvSurv(). This object
contains the estimated identifiable part of the joint distribution for the
potential outcomes.
Parameter vector for the sequence of unidentifiable
bivariate copulas that define the D-vine copula. The elements of
copula_par correspond to \((c_{23}, c_{13;2}, c_{24;3}, c_{14;23})\).
Copula family of the other bivariate copulas. For the
possible options, see loglik_copula_scale(). The elements of
copula_family2 correspond to \((c_{23}, c_{13;2}, c_{24;3}, c_{14;23})\).
Vector of rotation parameters for the sequence of
unidentifiable bivariate copulas that define the D-vine copula. The elements of
rotation_par correspond to \((c_{23}, c_{13;2},
c_{24;3}, c_{14;23})\).
Number of Monte Carlo samples for the computation of the mutual information.
Function that estimates the mutual information
between the first two arguments which are numeric vectors. Defaults to
FNN::mutinfo() with default arguments.
(boolean) If composite is TRUE, then the surrogate
endpoint is a composite of both a "pure" surrogate endpoint and the true
endpoint, e.g., progression-free survival is the minimum of time-to-progression
and time-to-death.
Seed for Monte Carlo sampling. This seed does not affect the global environment.
(numeric) Step size for finite difference in numeric differentiation
This function should not be used. The ICA is computed through numerical methods with a considerable error. This error is negligible in individual estimates of the ICA; however, this error easily breaks the numeric differentiation because finite differences are inflated by this error.