plot.gnm {gnm}

Plot Diagnostics for a gnm Object

Description

Five plots are available: a plot of residuals against fitted values, a Scale-Location plot of \sqrt{| residuals |} against fitted values, a Normal Q-Q plot, a plot of Cook's distances versus row labels, and a plot of residuals against leverages. By default, all except the fourth are produced.

Usage

## S3 method for class 'gnm'
plot(x, which = c(1:3, 5), caption = c("Residuals vs Fitted",
         "Normal Q-Q", "Scale-Location", "Cook's distance",
         "Residuals vs Leverage"),
         panel = if (add.smooth) panel.smooth else points,
         sub.caption = NULL, main = "",
         ask = prod(par("mfcol")) < length(which) && dev.interactive(),
         ..., id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75,
         qqline = TRUE, cook.levels = c(0.5, 1),
         add.smooth = getOption("add.smooth"), label.pos = c(4, 2),
         cex.caption = 1)    

Arguments

x

a "gnm" object.

which

a subset of the numbers 1:5 specifying which plots to produce (out of those listed in Description section).

caption

captions to appear above the plots.

panel

panel function. The useful alternative to points, panel.smooth can be chosen by add.smooth = TRUE.

sub.caption

common title - above figures if there are multiple; used as sub (s.title) otherwise. If NULL, as by default, a possible shortened version of deparse(x$call) is used.

main

title to each plot - in addition to the above caption.

ask

logical; if TRUE, the user is asked before each plot, see par(ask = .).

...

other parameters to be passed through to plotting functions.

id.n

number of points to be labelled in each plot starting with the most extreme.

labels.id

vector of labels, from which the labels for extreme points will be chosen. NULL uses observation numbers.

cex.id

magnification of point labels.

qqline

logical indicating if a qqline() should be added to the normal Q-Q plot.

cook.levels

levels of Cook's distance at which to draw contours.

add.smooth

logical indicating if a smoother should be added to most plots; see also panel above.

label.pos

positioning of labels, for the left half and right half of the graph respectively, for plots 1-3.

cex.caption

controls the size of 'caption'.

Details

sub.caption - by default the function call - is shown as a subtitle (under the x-axis title) on each plot when plots are on separate pages, or as a subtitle in the outer margin (if any) when there are multiple plots per page.

The "Scale-Location" plot, also called "Spread-Location" or "S-L" plot, takes the square root of the absolute residuals in order to diminish skewness (\sqrt{| E |} is much less skewed than | E | for Gaussian zero-mean E).

The S-L, the Q-Q, and the Residual-Leverage plot, use standardized residuals which have identical variance (under the hypothesis). They are given as R[i] / (s*\sqrt(1 - h_{ii})) where h_{ii} are the diagonal entries of the hat matrix, influence()$hat, see also hat.

The Residual-Leverage plot shows contours of equal Cook's distance, for values of cook.levels (by default 0.5 and 1) and omits cases with leverage one. If the leverages are constant, as typically in a balanced aov situation, the plot uses factor level combinations instead of the leverages for the x-axis.

Author(s)

Modification of plot.lm by the R Core Team. Adapted for "gnm" objects by Heather Turner.

See Also

gnm, plot.lm

Examples

set.seed(1)

##  Fit an association model with homogeneous row-column effects
RChomog <- gnm(Freq ~ origin + destination + Diag(origin, destination) +
               MultHomog(origin, destination), family = poisson,
               data = occupationalStatus)

## Plot model diagnostics
plot(RChomog)

## Put 4 plots on 1 page; allow room for printing model formula in outer margin:
par(mfrow = c(2, 2), oma = c(0, 0, 3, 0))
title <- paste(deparse(RChomog$formula, width.cutoff = 50), collapse = "\n")
plot(RChomog, sub.caption = title)

## Fit smoother curves
plot(RChomog, sub.caption = title, panel = panel.smooth)
plot(RChomog, sub.caption = title, panel = function(x,y) panel.smooth(x, y, span = 1))