discrimSS {sensR} | R Documentation |
Computes the sample size for any one of four methods: 2-AFC, 3-AFC, duotrio and triangle tests given the underlying sensory difference d, the type 1 test level and the required power.
discrimSS(delta, power, alpha = 0.05, method = c("duotrio", "threeAFC", "twoAFC", "triangle"), pd0 = 0, type = c("difference", "similarity"), start = 1)
delta |
the underlying sensory difference (larger than zero) |
power |
the wanted power (between zero and one) |
alpha |
the type 1 level of the test (between zero and one) |
method |
the discrimination protocol. Four allowed values: "twoAFC", "threeAFC", "duotrio", "triangle" |
pd0 |
the proportion of discriminators in the population of interest |
type |
the type of test to be conducted |
start |
lower bound on the sample size. Specifying start at
a value close to, but lower than the sample size will save
computational time. If a too high value is given, this value is
returned and the user can try a lower value for start . |
The sample size of the standard one-tailed difference test of "no
difference" is obtained with pd0 = 0
.
The probability under the null hypothesis is
given by pd0 + p0 * (1 - pd0)
where p0
is the guessing
probability defined by the method
argument.
The function uses one of the dedicated binomial families and the
discrimPwr
function
The sample size
Per Bruun Brockhoff and Rune Haubo B Christensen
Brockhoff, P.B. and Christensen, R.H.B (2010). Thurstonian models for sensory discrimination tests as generalized linear models. Food Quality and Preference, 21, pp. 330-338.
triangle
, twoAFC
,
threeAFC
, duotrio
,
discrim
, discrimSim
,
AnotA
, discrimPwr
,
samediff
, findcr
## Finding the necessary sample size: discrimSS(1, 0.9, 0.05, "twoAFC") discrimSS(1, 0.9, 0.05, "threeAFC") discrimSS(1, 0.9, 0.05, "duotrio") discrimSS(1, 0.9, 0.05, "triangle") ## A similarity example: discrimSS(.2, .80, method = "triangle", pd0 = .2, type = "simil")