| samediffPwr | R Documentation | 
Computes the power for at same-different discrimination experiment with a no-difference null hypothesis via simulation.
samediffPwr(n = 1000, tau, delta, Ns, Nd, alpha = 0.05)
n | 
 the number of samples to use in the simulation. More samples means higher precision, but takes longer to compute.  | 
tau | 
 the value of tau  | 
delta | 
 the underlying sensory difference under the alternative hypothesis (non-negative)  | 
Ns | 
 the number of same-samples (a positive integer)  | 
Nd | 
 the number of different-samples (a positive integer)  | 
alpha | 
 the type I level of the test (must be between zero and one)  | 
The power is computed using simulations. n datasets is
simulated from the
Same Different model with specified parameters. The power is the
fraction of times the p-value is lower than alpha.
Under some parameter combinations, there is a non-significant
probability that data will fall, so that the MLE of delta is
not defined and the p-value is not defined. All such undefined
p-values are silently ignored.
The estimated power may change between runs and especially if the power is either very large or very small (ie. close to 0 or 1). Using more simulations will provide higher accuracy.
It is often a good idea to run the power simulation a couple of times to ensure that the variation in the result is acceptable.
A single numeric value giving the power of the specified test.
Rune Haubo B Christensen
Christensen, R.H.B., Brockhoff, P.B. (2009). Estimation and inference in the same-different test. Food, Quality and Preference, 20 pp. 514–520
samediff, samediffSim
## Finding the power of a discrimination test with a sensory delta of 2
## (alternative hypothesis) versus a null hypothesis of delta = 0 with
## a sample of size 2 x 10 and a type I level of .05. n should be higher
## for a reasonable precision:
samediffPwr(n = 100, tau = 1, delta = 2, Ns = 10, Nd = 10)
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