plot_simASD: Plot Empirical Powers vs. Learn/Confirm Allocations under...
In gu-mi/simASD: Enhancement of the Adaptive Signature Design (ASD) for Learning and Confirming in a Single Pivotal Trial
Description
Usage
Arguments
Value
Author(s)
See Also
Description
This function takes an object from the return result of function f.pwr.cal, and makes
a ggplot2 object showing the empirical powers vs. learn/confirm allocations under different alpha-splitting scenarios.
The one-stage design power is also plotted as a horizontal dashed line for comparison.
Usage
1
plot_simASD(fpc.obj)
Arguments
fpc.obj
an object from the returned result of function f.pwr.cal
Value
a ggplot2 object with empirical power vs. learn/confirm allocation plot, with one-stage design power as a
horizontal dashed line for comparison
Author(s)
Gu Mi <neo.migu@gmail.com>
See Also
f.learn.confirm for the main function of learn/confirm analysis (taking on a mega dataset)
f.pwr.cal for calculating powers based on a mega dataset
gu-mi/simASD documentation built on May 17, 2019, 8:57 a.m.
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Description Usage Arguments Value Author(s) See Also
Description
This function takes an object from the return result of function f.pwr.cal, and makes
a ggplot2 object showing the empirical powers vs. learn/confirm allocations under different alpha-splitting scenarios.
The one-stage design power is also plotted as a horizontal dashed line for comparison.
Usage
1 | plot_simASD(fpc.obj)
|
Arguments
fpc.obj |
an object from the returned result of function |
Value
a ggplot2 object with empirical power vs. learn/confirm allocation plot, with one-stage design power as a horizontal dashed line for comparison
Author(s)
Gu Mi <neo.migu@gmail.com>
See Also
f.learn.confirm for the main function of learn/confirm analysis (taking on a mega dataset)
f.pwr.cal for calculating powers based on a mega dataset
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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