Description Usage Arguments Value Note Examples
Standard diagnostic plots.
1 2 3 4 5 
x 
A regression model with class 
y 
Not used. Present for compatibility with
generic 
... 
Additional arguments, which can be
passed to the plotting functions. See:

toPdf 

file 
Filename if writing to 
palette 
Palette of colors to use as the
'fill'/ 'background' colors for the plots.

usePalette 
Use the colorscheme in palette above.

bg 
The 'fill' or background color(s) to use, if

col 
The 'edge' or 'foreground' color used
to outline points in the plot.

alpha 
Transparency for colors above.

cex 
Character expansion.

pch 
Plotting character.

cex.main 
Character expansion for the plot title and the labels for the axes. 
inches 
Width of circles for the bubble plot. See

identify 
If 
devNew 
If 
There is one point per observation.
The following show probability P[i] on the xaxis:
P[i] vs. h[i] 
Probability vs. leverage. 
P[i] vs. dChisq[i] 
Probability vs. the change in the standardized Pearsons chisquared with observation i excluded. 
P[i] vs. dDev[i] 
Probability vs. the change in the standardized deviance with observation i excluded. 
P[i] vs. dBhat[i] 
Probability vs. the change in the standardized maximum likelihood estimators of the model coefficients with observation i excluded. 
P[i] vs. dChisq[i] 
Bubbleplot of
probability vs. the change in the standardized
Pearsons chisquared
with observation i excluded.
A[i] = pi r[i]^2, r[i] = (dBhat[i] / P[i])^0.5 For details see:

The following show leverage h[i] on the xaxis:
h[i] vs. dChisq[i] 
Leverage vs. the change in the standardized Pearsons chisquared with observation i excluded. 
h[i] vs. dDev[i] 
Leverage vs. the change in the standardized deviance with observation i excluded. 
h[i] vs. dBhat[i] 
Leverage vs. the change in the standardized maximum likelihood estimators of the model coefficients with observation i excluded. 
The correlation of
dChisq, dDev and dBhat.
is shown in a pairs
plot. See:
?graphics::pairs
The Value of dx
is also returned, invisibly.
A choice of colors can be found with e.g.
grDevices::colours()[grep("blue", grDevices::colours())]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## H&L 2nd ed. Table 4.9. Figures 5.55.8. Pages 177180.
data(uis)
uis < within(uis, {
NDRGFP1 < 10 / (NDRGTX + 1)
NDRGFP2 < NDRGFP1 * log((NDRGFP1 + 1) / 10)
})
summary(g1 < glm(DFREE ~ AGE + NDRGFP1 + NDRGFP2 + IVHX +
RACE + TREAT + SITE +
AGE:NDRGFP1 + RACE:SITE,
family=binomial, data=uis))
plot(g1)
## H&L. Similar to Figure 5.3.
set.seed(133)
(g1 < glm(sample(c(0, 1), size=100,
replace=TRUE, prob=c(0.5, 0.5))
~ 0 + I(0.08 * rnorm(n=100, mean=0, sd=sqrt(9))),
family=binomial))$coef # approx. 0.8
plot(g1)

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