sampleN.NTIDFDA(alpha = 0.05, targetpower = 0.8, theta0, theta1, theta2, CV,
design=c("2x2x4", "2x2x3"), nsims = 1e+05, nstart, print = TRUE,
details = TRUE, setseed = TRUE)
length(CV) = 1
the same CV is assumed for Test and Reference.
If length(CV) = 2
the CV for Test must be given in CV[1] and for
Reference in CV[2].design="2x2x4"
.TRUE
(default) the function prints its results.
If FALSE
only the resulting dataframe will be returned.TRUE
, the default, the steps during sample size search are shown.
Moreover the details of the method settings are printed.set.seed(123456)
is issued if setseed=TRUE
, the default.exp(+-1.053605*swR)
.
In case of a failed sample size search you may restart with setting tha argument nstart
.
In case of theta0 values outside the implied scaled (tightened/widened) ABE limits
no sample size estimation is possible and the function throws an error
(f.i. CV=0.04, theta0=0.95).
The results for the design "2x2x3" are to be considered as experimental since
at present not thorougly tested.power.NTIDFDA
sampleN.NTIDFDA(CV=0.04,theta0=0.975)
# should give
# n=54 with an (empirical) power of 0.809590
#
# Test formulation with lower variability
sampleN.NTIDFDA(CV=c(0.04,0.06),theta0=0.975)
# should give
# n=20 with an (empirical) power of 0.0.814610
#
# alternative 3-period design
sampleN.NTIDFDA(CV=0.04,theta0=0.975, design="2x2x3")
# should give
# n=86 with power = 0.80364
Run the code above in your browser using DataLab