getFinalConfidenceInterval {rpact}  R Documentation 
Returns the final confidence interval for the parameter of interest. It is based on the prototype case, i.e., the test for testing a mean for normally distributed variables.
getFinalConfidenceInterval(
design,
dataInput,
...,
directionUpper = TRUE,
thetaH0 = NA_real_,
tolerance = 1e06,
stage = NA_integer_
)
design 
The trial design. 
dataInput 
The summary data used for calculating the test results.
This is either an element of 
... 
Further (optional) arguments to be passed:

directionUpper 
Logical. Specifies the direction of the alternative,
only applicable for onesided testing; default is 
thetaH0 
The null hypothesis value,
default is
For testing a rate in one sample, a value 
tolerance 
The numerical tolerance, default is 
stage 
The stage number (optional). Default: total number of existing stages in the data input. 
Depending on design
and dataInput
the final confidence interval and median unbiased estimate
that is based on the stagewise ordering of the sample space will be calculated and returned.
Additionally, a nonstandardized ("general") version is provided,
the estimated standard deviation must be used to obtain
the confidence interval for the parameter of interest.
For the inverse normal combination test design with more than two stages, a warning informs that the validity of the confidence interval is theoretically shown only if no sample size change was performed.
Returns a list
containing
finalStage
,
medianUnbiased
,
finalConfidenceInterval
,
medianUnbiasedGeneral
, and
finalConfidenceIntervalGeneral
.
Other analysis functions:
getAnalysisResults()
,
getClosedCombinationTestResults()
,
getClosedConditionalDunnettTestResults()
,
getConditionalPower()
,
getConditionalRejectionProbabilities()
,
getFinalPValue()
,
getRepeatedConfidenceIntervals()
,
getRepeatedPValues()
,
getStageResults()
,
getTestActions()
design < getDesignInverseNormal(kMax = 2)
data < getDataset(
n = c(20, 30),
means = c(50, 51),
stDevs = c(130, 140)
)
getFinalConfidenceInterval(design, dataInput = data)