pvisual: P-value calculations.

In nullabor: Tools for Graphical Inference

P-value calculations.

Description

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.

Usage

pvisual(
  x,
  K,
  m = 20,
  N = 10000,
  type = "scenario3",
  xp = 1,
  target = 1,
  upper.tail = TRUE
)

Arguments

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.

Value

Vector/data frame. For comparison a p value based on a binomial distribution is provided as well.

Examples

pvisual(15, 20, m=3) # triangle test

nullabor documentation built on Sept. 11, 2024, 8:20 p.m.