These set of functions allow the user to calculate a p-value from the lineup after it has been evaluated by K independent observers. The different functions accommodate different lineup construction and showing to observers. Details are in the papers Majumder et al (2012) JASA, and Hofmann et al (2015). We distinguish between three different scenarios:
Scenario I: in each of K evaluations a different data set and a different set of (m-1) null plots is shown.
Scenario II: in each of K evaluations the same data set but a different set of (m-1) null plots is shown.
Scenario III: the same lineup, i.e. same data and same set of null plots, is shown to K different observers.
pvisual(x, K, m = 20, N = 10000, type = "scenario3", xp = 1, target = 1, upper.tail = TRUE)
| x | number of observed picks of the data plot |
|---|---|
| K | number of evaluations |
| m | size of the lineup |
| N | MC parameter: number of replicates on which MC probabilities are based. Higher number of replicates will decrease MC variability. |
| type | type of simulation used: scenario 3 assumes that the same lineup is shown in all K evaluations |
| xp | exponent used, defaults to 1 |
| target | integer value identifying the location of the data plot |
| upper.tail | compute probabilities P(X >= x). Be aware that the use of this parameter is not consistent with the other distribution functions in base. There, a value of P(X > x) is computed for upper.tail=TRUE. |
Vector/data frame. For comparison a p value based on a binomial distribution is provided as well.
pvisual(15, 20, m=3) # triangle test#> x simulated binom #> [1,] 15 0.0425 0.000167366