Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/influencePlot.mlm.R
This function creates various types of “bubble” plots of influence measures with the areas of the circles representing the observations proportional to Cook's distances.
type="stres"
plots squared (internally) Studentized residuals against hat values;
type="cookd"
plots Cook's distance against hat values;
type="LR"
plots residual components against leverage components,
with the property that contours of constant Cook's distance fall on diagonal
lines with slope = 1.
1 2 3 4 5 6 7 8  ## S3 method for class 'mlm'
influencePlot(model, scale = 12, type=c("stres", "LR", "cookd"),
infl = mlm.influence(model, do.coef = FALSE), FUN = det,
fill = TRUE, fill.col = "red", fill.alpha.max = 0.5,
labels,
id.method = "noteworthy", id.n = if (id.method[1] == "identify") Inf else 0,
id.cex = 1, id.col = palette()[1],
ref.col = "gray", ref.lty = 2, ref.lab = TRUE, ...)

model 
An 
scale 
a factor to adjust the radii of the circles, in relation to 
type 
Type of plot: one of 
infl 
influence measure structure as returned by 
FUN 
For 
labels, id.method, id.n, id.cex, id.col 
settings for labelling
points; see 
fill, fill.col, fill.alpha.max 

ref.col, ref.lty, ref.lab 
arguments for reference lines. Incompletely implemented in this version 
... 
other arguments passed down 
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
Barrett, B. E. and Ling, R. F. (1992). General Classes of Influence Measures for Multivariate Regression. Journal of the American Statistical Association, 87(417), 184191.
Barrett, B. E. (2003). Understanding Influence in Multivariate Regression Communications in Statistics  Theory and Methods, 32, 667680.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  data(Rohwer, package="heplots")
Rohwer2 < subset(Rohwer, subset=group==2)
Rohwer.mod < lm(cbind(SAT, PPVT, Raven) ~ n+s+ns+na+ss, data=Rohwer2)
influencePlot(Rohwer.mod, id.n=4, type="stres")
influencePlot(Rohwer.mod, id.n=4, type="LR")
influencePlot(Rohwer.mod, id.n=4, type="cookd")
# Sake data
data(Sake, package="heplots")
Sake.mod < lm(cbind(taste,smell) ~ ., data=Sake)
influencePlot(Sake.mod, id.n=3, type="stres")
influencePlot(Sake.mod, id.n=3, type="LR")
influencePlot(Sake.mod, id.n=3, type="cookd")
# Adopted data
data(Adopted, package="heplots")
Adopted.mod < lm(cbind(Age2IQ, Age4IQ, Age8IQ, Age13IQ) ~ AMED + BMIQ, data=Adopted)
influencePlot(Adopted.mod, id.n=3)
influencePlot(Adopted.mod, id.n=3, type="LR", ylim=c(4,1.5))

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