plot.cond: Generate Plots for an Approximate Conditional Inference...
In hoa: Higher Order Likelihood Inference

Description Usage Arguments Details Value Side Effects Note References See Also Examples

Creates a set of plots for an object of class cond.

## 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 `cond` object. This is assumed to be the result returned by the `cond.glm` function.
`from`	starting value for the x-axis range. The default value has been set by `cond.glm`.
`to`	ending value for the x-axis range. The default value has been set by `cond.glm`.
`which`	which plot should be printed. Admissible values are `2` to `8` corresponding to the choices in the menu below.
`alpha`	the level used to read off confidence intervals; default is 5%.
`add.leg`	if `TRUE`, a legend is added to each plot; default is `TRUE`.
`loc.leg`	if `TRUE`, position of the legend can be located by hand; default is `FALSE`.
`add.labs`	if `TRUE`, labels are added; default is `TRUE`.
`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 `par` for details.
`lwd1, lwd2`	line width used to compare different curves in the same plot; default is `lwd2 = 2` for higher order solutions and `lwd1 = 1` for first order solutions.
`lty1, lty2`	line type used to compare different curves in the same plot; default is `lty2 = "dashed"` for the Wald statistic and `lty1 = "solid"` for the remaining first- and higher order statistics.
`col1, col2`	colors used to compare different curves in the same plot; default is `col2 = "blue"` for higher order solutions, and `col1 = "black"` for the remaining first order statistics.
`tck, las, adj, lab`	further graphical parameters. See `par` for details.
`...`	optional graphical parameters; see `par` for details.

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:

 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: Lugannani-Rice 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 Small-Sample Parametric Inference, Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

cond.glm, cond.object, summary.cond

## 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)