Description Usage Arguments Side Effects Author(s) See Also Examples
From a linear (or glm) model fitted, produce the socalled TukeyAnscombe plot. Useful (optional) additions include: 0line, lowess smooth, 2sigma lines, and automatic labeling of observations.
1 2 3 4 5 6 7 8 9 10  TA.plot(lm.res,
fit= fitted(lm.res), res= residuals(lm.res, type="pearson"),
labels= NULL, main= mk.main(), xlab = "Fitted values",
draw.smooth= n >= 10, show.call = TRUE, show.2sigma= TRUE,
lo.iter = NULL, lo.cex= NULL,
par0line = list(lty = 2, col = "gray"),
parSmooth = list(lwd = 1.5, lty = 4, col = 2),
parSigma = list(lwd = 1.2, lty = 3, col = 4),
verbose = FALSE,
...)

lm.res 
Result of 
fit 
fitted values; you probably want the default here. 
res 
residuals to use. Default: Weighted ("Pearson") residuals if weights have been used for the model fit. 
labels 
strings to use as plotting symbols for each point.
Default( 
main 
main title to plot. Default: sophisticated, resulting in
something like "TukeyAnscombe Plot of : y \~ x" constructed from

xlab 
xaxis label for plot. 
draw.smooth 
logical; if 
show.call 
logical; if 
show.2sigma 
logical; if 
lo.iter 
positive integer, giving the number of lowess
robustness iterations. The default depends on the model and
is 
lo.cex 
character expansion ("cex") for lowess and other marginal texts. 
par0line 
a list of arguments (with reasonable defaults) to be passed to

parSmooth, parSigma 
each a list of arguments (with reasonable
default) for drawing the smooth curve (if 
verbose 
logical indicating if some construction details should
be reported ( 
... 
further graphical parameters are passed to

The above mentioned plot is produced on the current graphic device.
Martin Maechler, Seminar fuer Statistik, ETH Zurich, Switzerland; [email protected]
plot.lm
which also does a QQ normal plot and more.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  data(stackloss)
TA.plot(lm(stack.loss ~ stack.x))
example(airquality)
summary(lmO < lm(Ozone ~ ., data= airquality))
TA.plot(lmO)
TA.plot(lmO, label = "O") # instead of case numbers
if(FALSE) {
TA.plot(lm(cost ~ age+type+car.age, claims, weights=number, na.action=na.omit))
}
## for aov(.) : 
data(Gun, package = "nlme")
TA.plot( aov(rounds ~ Method + Physique/Team, data = Gun))
## Not so clear what it means for GLM, but: 
if(require(rpart)) { # for the two datasets only
data(solder, package = "rpart")
TA.plot(glm(skips ~ ., data = solder, family = poisson), cex= .6)
data(kyphosis, package = "rpart")
TA.plot(glm(Kyphosis ~ poly(Age,2) + Start, data=kyphosis, family = binomial),
cex=.75) # smaller title and plotting characters
}

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