| getDesignCharacteristics {rpact} | R Documentation |
Calculates the characteristics of a design and returns it.
getDesignCharacteristics(design = NULL, ...)
design |
The trial design. |
... |
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed. |
Calculates the inflation factor (IF), the expected reduction in sample size under H1, under H0, and under a value in between H0 and H1. Furthermore, absolute information values are calculated under the prototype case testing H0: mu = 0 against H1: mu = 1.
Returns a TrialDesignCharacteristics object.
The following generics (R generic functions) are available for this result object:
names() to obtain the field names,
print() to print the object,
summary() to display a summary of the object,
plot() to plot the object,
as.data.frame() to coerce the object to a data.frame,
as.matrix() to coerce the object to a matrix.
Click on the link of a generic in the list above to go directly to the help documentation of
the rpact specific implementation of the generic.
Note that you can use the R function methods to get all the methods of a generic and
to identify the object specific name of it, e.g.,
use methods("plot") to get all the methods for the plot generic.
There you can find, e.g., plot.AnalysisResults and
obtain the specific help documentation linked above by typing ?plot.AnalysisResults.
Other design functions:
getDesignConditionalDunnett(),
getDesignFisher(),
getDesignGroupSequential(),
getDesignInverseNormal(),
getGroupSequentialProbabilities(),
getPowerAndAverageSampleNumber()
## Not run:
# Calculate design characteristics for a three-stage O'Brien & Fleming
# design at power 90% and compare it with Pocock's design.
getDesignCharacteristics(getDesignGroupSequential(beta = 0.1))
getDesignCharacteristics(getDesignGroupSequential(beta = 0.1, typeOfDesign = "P"))
## End(Not run)