ICA_alpha_ContCont {Surrogate} | R Documentation |
Assess surrogacy using a Rényi divergence based family of metrics in the causal-inference single-trial setting in normal case
Description
The function ICA_alpha_ContCont()
is a set of metrics to evaluate surrogacy. ICA_alpha have the similar
mathematical properties with ICA.ContCont()
.
Usage
ICA_alpha_ContCont(
alpha = numeric(),
T0S0,
T1S1,
T0T0 = 1,
T1T1 = 1,
S0S0 = 1,
S1S1 = 1,
T0T1 = seq(-1, 1, by = 0.1),
T0S1 = seq(-1, 1, by = 0.1),
T1S0 = seq(-1, 1, by = 0.1),
S0S1 = seq(-1, 1, by = 0.1)
)
Arguments
alpha |
(numeric) is order |
T0S0 |
A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the control treatment condition |
T1S1 |
A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the control treatment condition |
T0T0 |
A scalar that specifies the variance of the true endpoint in the control treatment condition |
T1T1 |
A scalar that specifies the variance of the true endpoint in the control treatment condition |
S0S0 |
A scalar that specifies the variance of the true endpoint in the control treatment condition |
S1S1 |
A scalar that specifies the variance of the true endpoint in the control treatment condition |
T0T1 |
A scalar or vector that contains the correlation(s) between the counterfactuals T0 and T1 |
T0S1 |
A scalar or vector that contains the correlation(s) between the counterfactuals T0 and S1 |
T1S0 |
A scalar or vector that contains the correlation(s) between the counterfactuals T1 and S0 |
S0S1 |
A scalar or vector that contains the correlation(s) between the counterfactuals S0 and S1 |
Value
Total.Num.Matrices: An object of class numeric that contains the total number of matrices that can be formed as based on the user-specified correlations in the function call.
Pos.Def: A data.frame that contains the positive definite matrices that can be formed based on the user-specified correlations. These matrices are used to compute the vector of the
\rho_{\Delta}
values.rho: A scalar or vector that contains the individual causal association
\rho_{\Delta}
ICA: A scalar or vector that contains the individual causal association
\rho_{\Delta}^2=ICA
ICA_alpha: A scalar or vector that contains the individual causal association
ICA_{\alpha}
Sigmas: A data.frame that contains the
\sigma_{\Delta T}
and\sigma_{\Delta S}