lrPlot | R Documentation |
This function creates a “bubble” plot of functions, R = log(Studentized residuals^2) by L = log(H/p*(1-H)) of the hat values, with the areas of the circles representing the observations proportional to Cook's distances.
lrPlot(model, ...) ## S3 method for class 'lm' lrPlot( model, scale = 12, xlab = "log Leverage factor [log H/p*(1-H)]", 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 model object fit by |
... |
arguments to pass to the |
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 labeling 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. |
This plot, suggested by McCulloch & Meeter (1983) has the attractive property that contours of equal Cook's distance are diagonal 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.
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 side-effect 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. 181-189.
McCulloch, C. E. & Meeter, D. (1983). Discussion of "Outliers..." by R. J. Beckman and R. D. Cook. Technometrics, 25, 152-155.
influencePlot.mlm
influencePlot
in the car
package for other methods
# 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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