Description Usage Arguments Details Value Side Effects Note References See Also Examples
Creates a set of plots for an object of class cond
.
1 2 3 4 5 6 7  ## S3 method for class 'cond'
plot(x = stop("nothing to plot"), from = x.axis[1], to = x.axis[n],
which = NULL, alpha = 0.05, add.leg = TRUE, loc.leg = FALSE,
add.labs = TRUE, cex = 0.7, cex.lab = 1, cex.axis = 1,
cex.main = 1, lwd1 = 1, lwd2 = 2, lty1 = "solid",
lty2 = "dashed", col1 = "black", col2 = "blue", tck = 0.02,
las = 1, adj = 0.5, lab = c(15, 15, 5), ...)

x 
a 
from 
starting value for the xaxis range. The default value has been
set by 
to 
ending value for the xaxis range. The default value has been set
by 
which 
which plot should be printed. Admissible values are 
alpha 
the level used to read off confidence intervals; default is 5%. 
add.leg 
if 
loc.leg 
if 
add.labs 
if 
cex, cex.lab, cex.axis, cex.main 
character expansions relative to the standard size of the device
to be used for printing text, labels, axes and main title. See

lwd1, lwd2 
line width used to compare different curves in the same plot;
default is 
lty1, lty2 
line type used to compare different curves in the same plot;
default is 
col1, col2 
colors used to compare different curves in the same plot; default
is 
tck, las, adj, lab 
further graphical parameters. See 
... 
optional graphical parameters; see 
Several plots are produced for an object of class cond
. A
menu lists all the plots that can be produced. They may be one or
all of the following ones:
1 2 3 4 5 6 7 8 9 10 11 12 13  Make a plot selection (or 0 to exit)
1:plot: All
2:plot: Profile and modified profile log likelihoods
3:plot: Profile and modified profile likelihood ratios
4:plot: Profile and modified likelihood roots
5:plot: Modified and continuity corrected likelihood roots
6:plot: LugannaniRice approximations
7:plot: Confidence intervals
8:plot: Diagnostics based on INF/NP decomposition
Selection:

If no nuisance parameters are presented, a subset of the above pictures is produced. More details on the implementation are given in Brazzale (1999, 2000).
This function is a method for the generic function plot()
for class cond
. It can be invoked by calling plot
or directly plot.cond
for an object of the appropriate class.
A plot is created on the current graphics device.
The current device is cleared. When add.leg = TRUE
, a legend
is added to each plot, and if loc.leg = TRUE
, it can be set
by the user. All screens are closed, but not cleared, on
termination of the function.
The diagnostic plots only represent a preliminary version and need further development.
The two panels on the right trace the information and nuisance correction terms, INF and NP, against the likelihood root statistic. These are generally smooth functions and used to approximate the information and nuisance parameter aspects as a function of the parameter of interest, as shown in the two panels on the left. This procedure has the advantage of largely eliminating the numerical instabilities that affect the statistics around the MLE. The circles in the two leftmost panels represent the limit of INF and NP at the MLE calculated exactly using numerical derivatives. All four pictures are intended to give an idea of the order of magnitude of the two correction terms while trying to deal with the numerical problems that likely occur for these kinds of data.
More details can be found in Brazzale (2000, Appendix B.2).
Brazzale, A. R. (1999) Approximate conditional inference for logistic and loglinear models. J. Comput. Graph. Statist., 8, 1999, 653–661.
Brazzale, A. R. (2000) Practical SmallSample Parametric Inference, Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.
cond.glm
, cond.object
,
summary.cond
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## Crying Babies Data
data(babies)
babies.glm < glm(formula = cbind(r1, r2) ~ day + lull  1,
family = binomial, data = babies)
babies.cond < cond(object = babies.glm, offset = lullyes)
## Not run:
plot(babies.cond)
## End(Not run)
## Urine Data
data(urine)
urine.glm < glm(r ~ I(gravity * 100) + ph + osmo + conduct + urea + calc,
family = binomial, data = urine)
urine.cond < cond(urine.glm, I(gravity * 100))
plot(urine.cond, which=4)

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