MaxEntContCont {Surrogate} | R Documentation |
Use the maximum-entropy approach to compute ICA in the continuous-continuous sinlge-trial setting
Description
In a surrogate evaluation setting where both S
and T
are continuous
endpoints, a sensitivity-based approach where multiple 'plausible values' for ICA are retained can be used (see functions ICA.ContCont
). The function MaxEntContCont
identifies the estimate which has the maximuum entropy.
Usage
MaxEntContCont(x, T0T0, T1T1, S0S0, S1S1)
Arguments
x |
A fitted object of class |
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 experimental treatment condition. |
S0S0 |
A scalar that specifies the variance of the surrogate endpoint in the control treatment condition. |
S1S1 |
A scalar that specifies the variance of the surrogate endpoint in the experimental treatment condition. |
Value
ICA.Max.Ent |
The ICA value with maximum entropy. |
Max.Ent |
The maximum entropy. |
Entropy |
The vector of entropies corresponding to the vector of 'plausible values' for ICA. |
Table.ICA.Entropy |
A |
ICA.Fit |
The fitted |
Author(s)
Wim Van der Elst, Ariel Alonso, Paul Meyvisch, & Geert Molenberghs
References
Add
See Also
Examples
## Not run: #time-consuming code parts
# Compute ICA for ARMD dataset, using the grid
# G={-1, -.80, ..., 1} for the undidentifiable correlations
ICA <- ICA.ContCont(T0S0 = 0.769, T1S1 = 0.712, S0S0 = 188.926,
S1S1 = 132.638, T0T0 = 264.797, T1T1 = 231.771,
T0T1 = seq(-1, 1, by = 0.2), T0S1 = seq(-1, 1, by = 0.2),
T1S0 = seq(-1, 1, by = 0.2), S0S1 = seq(-1, 1, by = 0.2))
# Identify the maximum entropy ICA
MaxEnt_ARMD <- MaxEntContCont(x = ICA, S0S0 = 188.926,
S1S1 = 132.638, T0T0 = 264.797, T1T1 = 231.771)
# Explore results using summary() and plot() functions
summary(MaxEnt_ARMD)
plot(MaxEnt_ARMD)
plot(MaxEnt_ARMD, Entropy.By.ICA = TRUE)
## End(Not run)