This function creates a “bubble” plot of functions, R = log(Studentized residuals^2) by L = log(H/p*(1H)) of the hat values, with the areas of the circles representing the observations proportional to Cook's distances.
This plot, suggested by McCulloch & Meeter (1983) has the attractive property that contours of equal Cook's distance are diagnonal lines with slope = 1. Various reference lines are drawn on the plot corresponding to twice and three times the average hat value, a “large” squared studentized residual and contours of Cook's distance.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  lrPlot(model, ...)
## S3 method for class 'lm'
lrPlot(model, scale = 12,
xlab = "log Leverage factor [log H/p*(1H)]",
ylab = "log (Studentized Residual^2)",
xlim = NULL, ylim,
labels,
id.method = "noteworthy",
id.n = if (id.method[1] == "identify") Inf else 0,
id.cex = 1, id.col = palette()[1],
ref = c("h", "v", "d", "c"), ref.col = "gray",
ref.lty = 2, ref.lab = TRUE,
...)

model 
a linear or generalizedlinear model. 
scale 
a factor to adjust the radii of the circles, in relation to 
xlab, ylab 
axis labels. 
xlim, ylim 
Limits for x and y axes. In the space of (L, R) very small residuals
typically extend the y axis enough to swamp the large residuals, so the default for

labels, id.method, id.n, id.cex, id.col 
settings for labelling
points; see 
ref 
Options to draw reference lines, any one or more of 
ref.col, ref.lty 
Color and line type for reference lines. Reference lines for 
ref.lab 
A logical, indicating whether the reference lines should be labeled. 
... 
arguments to pass to the 
The id.method="noteworthy"
setting
also requires setting id.n>0
to have any effect.
Using id.method="noteworthy"
, and id.n>0
, the number of points labeled
is the union of the largest id.n
values on each of L, R, and CookD.
If points are identified, returns a data frame with the hat values, Studentized residuals and Cook's distance of the identified points. If no points are identified, nothing is returned. This function is primarily used for its sideeffect of drawing a plot.
Michael Friendly
A. J. Lawrence (1995). Deletion Influence and Masking in Regression Journal of the Royal Statistical Society. Series B (Methodological) , Vol. 57, No. 1, pp. 181189.
McCulloch, C. E. & Meeter, D. (1983). Discussion of "Outliers..." by R. J. Beckman and R. D. Cook. Technometrics, 25, 152155.
influencePlot
in the car
package for other methods
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 26  # artificial example from Lawrence (1995)
x < c( 0, 0, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 18, 18 )
y < c( 0, 6, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 7, 18 )
DF < data.frame(x,y, row.names=LETTERS[1:length(x)])
DF
with(DF, {
plot(x,y, pch=16, cex=1.3)
abline(lm(y~x), col="red", lwd=2)
NB < c(1,2,13,14)
text(x[NB],y[NB], LETTERS[NB], pos=c(4,4,2,2))
}
)
mod < lm(y~x, data=DF)
# standard influence plot from car
influencePlot(mod, id.n=4)
# lrPlot version
lrPlot(mod, id.n=4)
library(car)
dmod < lm(prestige ~ income + education, data = Duncan)
influencePlot(dmod, id.n=3)
lrPlot(dmod, id.n=3)

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