View source: R/Bootstrap.MEP.BinBin.R
Bootstrap.MEP.BinBin | R Documentation |
Computes a 95% bootstrap-based CI around the maximum-entropy ICA and SPF (surrogate predictive function) in the binary-binary setting
Bootstrap.MEP.BinBin(Data, Surr, True, Treat, M=100, Seed=123)
Data |
The dataset to be used. |
Surr |
The name of the surrogate variable. |
True |
The name of the true endpoint. |
Treat |
The name of the treatment indicator. |
M |
The number of bootstrap samples taken. Default |
Seed |
The seed to be used. Default |
R2H |
The vector the bootstrapped MEP ICA values. |
r_1_1 |
The vector of the bootstrapped bootstrapped MEP |
r_min1_1 |
The vector of the bootstrapped bootstrapped MEP |
r_0_1 |
The vector of the bootstrapped bootstrapped MEP |
r_1_0 |
The vector of the bootstrapped bootstrapped MEP |
r_min1_0 |
The vector of the bootstrapped bootstrapped MEP |
r_0_0 |
The vector of the bootstrapped bootstrapped MEP |
r_1_min1 |
The vector of the bootstrapped bootstrapped MEP |
r_min1_min1 |
The vector of the bootstrapped bootstrapped MEP |
r_0_min1 |
The vector of the bootstrapped bootstrapped MEP |
vector_p |
The matrix that contains all bootstrapped maximum entropy distributions of the vector of the potential outcomes. |
Wim Van der Elst, Ariel Alonso, & Geert Molenberghs
Alonso, A., & Van der Elst, W. (2015). A maximum-entropy approach for the evluation of surrogate endpoints based on causal inference.
ICA.BinBin
, ICA.BinBin.Grid.Sample
, ICA.BinBin.Grid.Full
, plot MaxEntSPF BinBin
## Not run: # time consuming code part
MEP_CI <- Bootstrap.MEP.BinBin(Data = Schizo_Bin, Surr = "BPRS_Bin", True = "PANSS_Bin",
Treat = "Treat", M = 500, Seed=123)
summary(MEP_CI)
## End(Not run)
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