| profile.discrim {sensR} | R Documentation |
Computes the (normalized or relative) profile likelihood for the parameters of a discrimination test, plots the normalized profile likelihood and computes profile likelihood confidence intervals.
## S3 method for class 'discrim':
profile(fitted, min = 0, max = 3, numpts = 50, ...)
## S3 method for class 'profile.discrim':
plot(x, level = c(0.99, 0.95), fig = TRUE,
method = "natural", n = 500, ...)
## S3 method for class 'discrim':
confint(object, parm, level = 0.95, ...)
fitted |
a discrim object. |
x |
a profile.discrim object. |
object |
a discrim object. |
parm |
currently not used. |
min |
the minimum delta for which to do the profiling. By default set to 0, which for numerical stability is change internally to 1e-4. |
max |
the maximum delta beyond the MLE for which to do the profiling. |
numpts |
control parameter: At how many points should the profile likelihood be evaluated? |
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. For
confint: additional arguments to confint.glm in
package MASS. For profile: additional arguments to
glm. |
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:
An object of class "nProfile.discrim", "data.frame"—the
data.frame from the profile-object with an extra
columns containing the
normalized profile likelihood.
For confint:
A 2x2 matrix with columns named "lower", "upper" giving the
lower and upper (1 - alpha)% confidence interval for the
parameters named in the rows.
Rune Haubo B Christensen and Per Bruun Brockhoff
Brockhoff, P.B. and Christensen R.H.B.(2008). Thurstonian models for sensory discrimination tests as generalized linear models. Manuscript for Food Quality and Preference.
## 7 success out of 10 samples in a duo-trio experiment: dd <- discrim(7, 10, "duotrio") plot(profile(dd)) confint(dd) points(confint(dd), rep(.1465, 2), pch = 3)