sample_deltas_BinCont {Surrogate} | R Documentation |
Sample individual casual treatment effects from given D-vine copula model in binary continuous setting
Description
Sample individual casual treatment effects from given D-vine copula model in binary continuous setting
Usage
sample_deltas_BinCont(
copula_par,
rotation_par,
copula_family1,
copula_family2 = copula_family1,
n,
q_S0 = NULL,
q_S1 = NULL,
q_T0 = NULL,
q_T1 = NULL,
marginal_sp_rho = TRUE,
setting = "BinCont",
composite = FALSE,
plot_deltas = FALSE,
restr_time = +Inf
)
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 |
Number of samples to be taken from the D-vine copula. |
q_S0 |
Quantile function for the distribution of |
q_S1 |
Quantile function for the distribution of |
q_T0 |
Quantile function for the distribution of |
q_T1 |
Quantile function for the distribution of |
marginal_sp_rho |
(boolean) Compute the sample Spearman correlation
matrix? Defaults to |
setting |
Should be one of the following two:
|
composite |
(boolean) If |
plot_deltas |
Plot the sampled individual causal effects? Defaults to
|
restr_time |
Restriction time for the potential outcomes. Defaults to
|
Value
A list with two elements:
-
Delta_dataframe
: a dataframe containing the sampled individual causal treatment effects -
marginal_sp_rho_matrix
: a matrix containing the marginal pairwise Spearman's rho parameters estimated from the sample. Ifmarginal_sp_rho = FALSE
, this matrix is not computed andNULL
is returned for this element of the list.