AnotA: Analysis of A-not-A tests In sensR: Thurstonian Models for Sensory Discrimination

Description

Computation of dprime and it's uncertainty for the monadic A-not-A test together with the one-tailed P-value of the difference test (Fisher's Exact test).

Usage

 1 2 3 4 5 6 7 AnotA(x1, n1, x2, n2, ...) ## S3 method for class 'anota' confint(object, parm, level = 0.95, ...) ## S3 method for class 'anota' plot(x, main = TRUE, length = 1000, ...)

Arguments

 x1 the number of (correct) A-answers on A-samples n1 the total number of A-samples x2 the number of A-answers on not-A-samples n2 the number of not-A-samples object an anota object parm currently not used level the desired confidence level x an anota object main should the plot have a main title? length the discretization of the curves ... additional arguments passed to glm for AnotA; not used for confint and plot

Details

The AnotA function uses the glm and fisher.test functions of the stats package. Note that all arguments have to be positive integers.

Value

For AnotA an object of class anota (which has a print method). This is a list with elements

 coefficients named vector of coefficients (d-prime) res.glm the glm-object from the fitting process vcov variance-covariance matrix of the coefficients se named vector with standard error of the coefficients (standard error of d-prime data a named vector with the data supplied to the function p.value one-sided p-value from Fisher's exact test (fisher.test) test a string with the name of the test (A-Not A) for the print method call the matched call

For plot a figure of the distributions of sensory intensity is produced, and for confint a 2-by-2 matrix of confidence intervals is returned.

Author(s)

Rune Haubo B Christensen and Per Bruun Brockhoff

References

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.