profile.discrim {sensR}

Profile likelihood and confidence interval methods for discrim objects

Description

Computes the (normalized or relative) profile likelihood for the parameters of a discrimination test, plots the normalized profile likelihood.

Usage

## S3 method for class 'discrim'
profile(fitted, ...)

## S3 method for class 'profile.discrim'
plot(x, level = c(0.99, 0.95), fig = TRUE,
            method = "natural", n = 1e3, ...)

## S3 method for class 'discrim'
confint(object, parm, level = 0.95, ...)

Arguments

fitted

a discrim object

x

a profile.discrim object

object

a discrim object

parm

currently not used

method

the type of spline to be used in approximating the profile likelhood curve (trace)—se spline for details

n

the number of spline interpolations to use in plotting the profile likelihood curve (trace)

level

for plot: At which levels to include horizontal lines to indicate confidence levels in plots of the normalized profile likelihoods. For confint: at which level to compute the confidence interval

fig

logical: should the normalized profile likelihoods be plotted?

...

For plot: additional arguments to plot. Otherwise not used.

Details

confint returns the confidence interval computed in discrim possibly at another level. The statistic used to compute the confidence interval is therefore determined in the discrim call and may not be the likelihood root.

The likelihood profile is extracted from the discrim object fitted with statistic = "likelihood".

Value

For profile: An object of class "profile.discrim", "data.frame"—a data.frame with two columns giving the value of the parameter and the corresponding value of the profile likelihood.

For plot: The profile object is returned invisibly.

For confint:

A 3x2 matrix with columns named "lower", "upper" giving the lower and upper (100 * level)% confidence interval for the parameters named in the rows.

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.

See Also

discrim

Examples

## 7 success out of 10 samples in a duo-trio experiment:
(dd <- discrim(7, 10, method = "duotrio", statistic = "likelihood"))
confint(dd)
plot(profile(dd))
points(confint(dd)[3,], rep(.1465, 2), pch = 3, cex = 2, lwd=2)