mvinfluence (version 0.9.4)

influencePlot.mlm: Influence Plots for Multivariate Linear Models

Description

This function creates various types of “bubble” plots of influence measures with the areas of the circles representing the observations proportional to generalized Cook's distances.

Usage

# S3 method for 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,
  ...
)

Value

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.

Details

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 attractive property that contours of constant Cook's distance fall on diagonal lines with slope = -1. Adjacent reference lines represent multiples of influence.

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.

References

Barrett, B. E. and Ling, R. F. (1992). General Classes of Influence Measures for Multivariate Regression. Journal of the American Statistical Association, 87(417), 184-191.

Barrett, B. E. (2003). Understanding Influence in Multivariate Regression Communications in Statistics - Theory and Methods, 32, 667-680.

McCulloch, C. E. & Meeter, D. (1983). Discussion of "Outliers..." by R. J. Beckman and R. D. Cook. Technometrics, 25, 152-155

See Also

mlm.influence, lrPlot

influencePlot in the car package

Examples

data(Rohwer, package="heplots")
Rohwer2 <- subset(Rohwer, subset=group==2)
Rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ n+s+ns+na+ss, data=Rohwer2)

# Types of influence plots
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))

# schooldata 
data(schooldata, package = "heplots")
school.mod <- lm(cbind(reading, mathematics, selfesteem) ~ ., 
                 data=schooldata)

influencePlot(school.mod, id.n=4, type="stres")

influencePlot(school.mod, id.n=4, type="LR")