Description Usage Arguments Details Value Author(s) References See Also Examples

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).

1 2 3 4 5 6 7 |

`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 |

`parm` |
currently not used |

`level` |
the desired confidence level |

`x` |
an |

`main` |
should the plot have a main title? |

`length` |
the discretization of the curves |

`...` |
additional arguments passed to |

The `AnotA`

function uses the `glm`

and `fisher.test`

functions of the `stats`

package. Note that all arguments have
to be positive integers.

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
( |

`test` |
a string with the name of the test ( |

`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.

Rune Haubo B Christensen and Per Bruun Brockhoff

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.

`print.discrim`

, `discrim`

,
`discrimPwr`

, `discrimSim`

,
`discrimSS`

, `findcr`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
# data: 10 of the A-samples were judged to be A
# 20 A-samples in total
# 3 of the not-A samples were judged to be A
# 20 not-A-samples in total
AnotA(10, 20, 3, 20)
(m1 <- AnotA(10, 20, 3, 20))
## plot distributions of sensory intensity:
plot(m1)
## likelihood based confidence intervals:
confint(m1)
## Extended example plotting the profile likelihood
xt <- cbind(c(3, 10), c(20 - 3, 20 - 10))
lev <- gl(2, 1)
summary(res <- glm(xt ~ lev,
family = binomial(link = probit)))
N <- 100
dev <- double(N)
level <- c(0.95, 0.99)
delta <- seq(1e-4, 5, length = N)
for(i in 1:N)
dev[i] <- glm(xt ~ 1 + offset(c(0, delta[i])),
family = binomial(probit))$deviance
plot(delta, exp(-dev/2), type = "l",
xlab = expression(delta),
ylab = "Normalized Profile Likelihood")
## Add Normal approximation:
lines(delta, exp(-(delta - coef(res)[2])^2 /
(2 * vcov(res)[2,2])), lty = 2)
## Add confidence limits:
lim <- sapply(level, function(x)
exp(-qchisq(x, df=1)/2) )
abline(h = lim, col = "grey")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.