Description Usage Arguments Details Value Author(s) Examples

Transforms or rescales estimates and optionally standard
errors between the three levels at which a sensory difference is
measured: pc (proportion of correct answers), pd (proportion of
discriminators) and d-prime. `rescale`

is the main function and
only one of pc, pd or d-prime should be given as argument — values
for the remaining two scales will be computed.

A number of auxiliary functions are also provided:

`psyfun`

implements the psychometric functions and maps from d-prime to pc

`psyinv`

implements the inverse psychometric functions and maps from pc to d-prime

`psyderiv`

implements the derivative of the psychometric functions

`pc2pd`

maps from pc to pd

`pd2pc`

maps from pd to pc

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
rescale(pc, pd, d.prime, std.err,
method = c("duotrio", "tetrad", "threeAFC", "twoAFC",
"triangle", "hexad", "twofive", "twofiveF"),
double = FALSE)
psyfun(d.prime, method = c("duotrio", "tetrad", "threeAFC", "twoAFC",
"triangle", "hexad", "twofive", "twofiveF"),
double = FALSE)
psyinv(pc, method = c("duotrio", "tetrad", "threeAFC", "twoAFC",
"triangle", "hexad", "twofive", "twofiveF"),
double = FALSE)
psyderiv(d.prime, method = c("duotrio", "tetrad", "threeAFC", "twoAFC",
"triangle", "hexad", "twofive", "twofiveF"),
double = FALSE)
pc2pd(pc, Pguess)
pd2pc(pd, Pguess)
``` |

`pc` |
the proportion of correct answers; a numerical vector between 0 and 1 |

`pd` |
the proportion of discriminators; a numerical vector between 0 and 1 |

`d.prime` |
the sensory difference on the d-prime scale; a non-negative numerical vector. |

`std.err` |
optional numerical vector of standard errors of the
same length as the either of |

`method` |
the sensory discrimination protocol for which the results should apply |

`double` |
should the 'double' variant of the discrimination protocol be used? Logical scalar. |

`Pguess` |
the guessing probability implied by the protocol; a numeric scalar between 0 and 1 |

The `rescale`

function is based on the fact that once the
protocol and one of
pc, pd and d-prime is known, the other two can be computed. The same
applies to the standard errors of these parameters.

Standard errors are optional, but if they are supplied, the length of
the `std.err`

argument has to match the length of `pc`

,
`pd`

or `d.prime`

whichever is given.

A `print`

method is implemented for `rescale`

objects.

For `rescale`

an object of class `rescale`

with elements

`coefficients` |
a |

`std.err` |
if standard errors are given trough the |

`method` |
the sensory discrimination protocol for which the results apply |

For `psyfun`

, `psyinv`

, `psyderiv`

, `pc2pd`

and
`pd2pc`

a numerical vector of the same length as the first
argument with appropriate contents.

Rune Haubo B Christensen

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## suppose 15 out of 20 are observed in a duo-trio experiment, then
## the estimated probability of correct a answer is
(pc <- 15/20)
## The standard error of this estimate is
(se.pc <- sqrt(pc * (1 - pc) / 20))
## The corresponding estimate of proportion of discriminators (pd) and
## d-prime with associated standard errors are:
rescale(pc = pc, std.err = se.pc, method = "duotrio")
## Can also do
rescale(pd = c(.6,.7), std.err = c(.2, NA))
psyfun(2, method = "triangle")
psyinv(0.8, method = "twoAFC")
psyderiv(2, method = "duotrio")
pc2pd(0.7, 1/2)
pd2pc(0.3, 1/3)
``` |

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