getDesignFisher {rpact}  R Documentation 
Performs Fisher's combination test and returns critical values for this design.
getDesignFisher( ..., kMax = NA_integer_, alpha = NA_real_, method = c("equalAlpha", "fullAlpha", "noInteraction", "userDefinedAlpha"), userAlphaSpending = NA_real_, alpha0Vec = NA_real_, informationRates = NA_real_, sided = 1, bindingFutility = NA, tolerance = 1e14, iterations = 0L, seed = NA_real_ )
... 
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed. 
kMax 
The maximum number of stages 
alpha 
The significance level alpha, default is 
method 

userAlphaSpending 
The user defined alpha spending.
Numeric vector of length 
alpha0Vec 
Stopping for futility bounds for stagewise pvalues. 
informationRates 
The information rates (that must be fixed prior to the trial),
default is 
sided 
Is the alternative onesided ( 
bindingFutility 
If 
tolerance 
The numerical tolerance, default is 
iterations 
The number of simulation iterations, e.g.,

seed 
Seed for simulating the power for Fisher's combination test. See above, default is a random seed. 
getDesignFisher
calculates the critical values and stage levels for
Fisher's combination test as described in Bauer (1989), Bauer and Koehne (1994),
Bauer and Roehmel (1995), and Wassmer (1999) for equally and unequally sized stages.
Returns a TrialDesign
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
,
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
.
getDesignSet
for creating a set of designs to compare.
Other design functions:
getDesignCharacteristics()
,
getDesignConditionalDunnett()
,
getDesignGroupSequential()
,
getDesignInverseNormal()
,
getPowerAndAverageSampleNumber()
# Calculate critical values for a twostage Fisher's combination test # with full level alpha = 0.05 at the final stage and stopping for # futility bound alpha0 = 0.50, as described in Bauer and Koehne (1994). getDesignFisher(kMax = 2, method = "fullAlpha", alpha = 0.05, alpha0Vec = 0.50)