plot.marg: Generate Plots for an Approximate Marginal Inference Object
In marg: Approximate Marginal Inference for Regression-Scale Models

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

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

## S3 method for class 'marg'
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 `marg` object. This is assumed to be the result returned by the `cond.rsm` function.
`from`	the starting value for the x-axis range. The default value has been set by `cond.rsm`.
`to`	the ending value for the x-axis range. The default value has been set by `cond.rsm`.
`which`	which plot to print. Admissible values are `2` to `7` corresponding to the choices in the menu below.
`alpha`	the level used to read off confidence intervals; the default is 5%.
`add.leg`	if `TRUE`, a legend is added to each plot; default is `TRUE`.
`loc.leg`	if `TRUE`, the 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`	the 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`	the line widths 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 marg. 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: All
2: Profile and modified profile log likelihoods
3: Profile and modified profile likelihood ratios
4: Profile and modified likelihood root
5: Lugannani-Rice approximation
6: Confidence intervals
7: Diagnostics based on INF/NP decomposition

Selection:

If no nuisance parameters are presented, a subset of the above pictures is produced. A message is printed if this is the case. More details and examples are given in Brazzale (2000, Sections 6.5 and 5.3.2).

This function is a method for the generic function plot() for class marg. It can be invoked by calling plot or directly plot.marg for an object of the appropriate class.

A plot is created on the current graphics device.

The current device is cleared. When add.leg is TRUE, a legend is added to each plot. Furthermore, if loc.leg is TRUE, the location of the legend can be set by the user. All screens are closed, but not cleared, on termination of the function.

If the parameter of interest is the scale parameter, all calculations are performed on the log scale, though most results are reported on the original scale.

Four diagnostic plots are provided. 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. 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. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

cond.rsm, marg.object, summary.marg

# Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
#
# quadratic model fitted to the sea level, includes 18.62-year 
# astronomical tidal cycle and 11-year sunspot cycle
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
venice.marg <- cond(venice.rsm, I(Year^2))
plot(venice.marg, which = 4)
##
detach()