plot.profile {stats}  R Documentation 
Plotting Functions for 'profile' Objects
Description
plot
and pairs
methods for objects of
class "profile"
.
Usage
## S3 method for class 'profile'
plot(x, ...)
## S3 method for class 'profile'
pairs(x, colours = 2:3, which = names(x), ...)
Arguments
x 
an object inheriting from class 
colours 
colours to be used for the mean curves conditional on

which 
names or number of parameters in pairs plot 
... 
arguments passed to or from other methods. 
Details
This is the main plot
method for objects created by
profile.glm
. It can also be called on objects created
by profile.nls
, but they have a specific method,
plot.profile.nls
.
The pairs
method shows, for each pair of parameters x and
y, two curves intersecting at the maximum likelihood estimate, which
give the loci of the points at which the tangents to the contours of
the bivariate profile likelihood become vertical and horizontal,
respectively. In the case of an exactly bivariate normal profile
likelihood, these two curves would be straight lines giving the
conditional means of yx and xy, and the contours would be exactly
elliptical. The which
argument allows you to select a subset
of parameters; the default corresponds to the set of parameters that have
been profiled.
Author(s)
Originally, D. M. Bates and W. N. Venables for S (in 1996). Taken from MASS where these functions were rewritten by B. D. Ripley for R (by 1998).
See Also
Examples
## see ?profile.glm for another example using glm fits.
## a version of example(profile.nls) from R >= 2.8.0
fm1 < nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
pr1 < profile(fm1, alphamax = 0.1)
stats:::plot.profile(pr1) ## override dispatch to plot.profile.nls
pairs(pr1) # a little odd since the parameters are highly correlated
## an example from ?nls
x < (1:100)/10
y < 100 + 10 * exp(x / 2) + rnorm(x)/10
nlmod < nls(y ~ Const + A * exp(B * x), start=list(Const=100, A=10, B=1))
pairs(profile(nlmod))
## example from Dobson (1990) (see ?glm)
counts < c(18,17,15,20,10,20,25,13,12)
outcome < gl(3,1,9)
treatment < gl(3,3)
## this example is only formally a Poisson model. It is really a
## comparison of 3 multinomials. Only the interaction parameters are of
## interest.
glm.D93i < glm(counts ~ outcome * treatment, family = poisson())
pr1 < profile(glm.D93i)
pr2 < profile(glm.D93i, which=6:9)
plot(pr1)
plot(pr2)
pairs(pr1)
pairs(pr2)