robmlm: Robust Fitting of Multivariate Linear Models

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Description

Fit a multivariate linear model by robust regression using a simple M estimator.

These S3 methods are designed to provide a specification of a class of robust methods which extend mlms, and are therefore compatible with other mlm extensions, including Anova and heplot.

Usage

robmlm(X, ...)

## S3 method for class 'formula'
robmlm(formula, data, subset, weights, na.action, 
	model = TRUE, contrasts = NULL, ...)

## Default S3 method:
robmlm(X, Y, w, 
	P = 2 * pnorm(4.685, lower.tail = FALSE), tune, max.iter = 100, 
	psi = psi.bisquare, tol = 1e-06, initialize, verbose = FALSE, ...)


## S3 method for class 'robmlm'
print(x, ...)

## S3 method for class 'summary.robmlm'
print(x, ...)

## S3 method for class 'robmlm'
summary(object, ...)

Arguments

formula	a formula of the form cbind(y1, y2, ...) ~ x1 + x2 + ....
data	a data frame from which variables specified in formula are preferentially to be taken.
subset	An index vector specifying the cases to be used in fitting.
weights	a vector of prior weights for each case.
na.action	A function to specify the action to be taken if NAs are found. The 'factory-fresh' default action in R is na.omit, and can be changed by options(na.action=).
model	should the model frame be returned in the object?
contrasts	optional contrast specifications; see lm for details.
...	other arguments, passed down. In particular relevant control arguments can be passed to the to the robmlm.default method.
X	for the default method, a model matrix, including the constant (if present)
Y	for the default method, a response matrix
w	prior weights
P	two-tail probability, to find cutoff quantile for chisq (tuning constant); default is set for bisquare weight function
tune	tuning constant (if given directly)
max.iter	maximum number of iterations
psi	robustness weight function; psi.bisquare is the default
tol	convergence tolerance, maximum relative change in coefficients
initialize	modeling function to find start values for coefficients, equation-by-equation; if absent WLS (lm.wfit) is used
verbose	show iteration history? (TRUE or FALSE)
x	a robmlm object
object	a robmlm object

Details

Fitting is done by iterated re-weighted least squares (IWLS), using weights based on the Mahalanobis squared distances of the current residuals from the origin, and a scaling (covariance) matrix calculated by cov.trob. The design of these methods were loosely modeled on rlm.

An internal vcov.mlm function is an extension of the standard vcov method providing for observation weights.

Value

An object of class "robmlm" inheriting from c("mlm", "lm").

This means that the returned "robmlm" contains all the components of "mlm" objects described for lm, plus the following:

weights	final observation weights
iterations	number of iterations
converged	logical: did the IWLS process converge?

The generic accessor functions coefficients, effects, fitted.values and residuals extract various useful features of the value returned by robmlm.

Author(s)

John Fox; packaged by Michael Friendly

References

A. Marazzi (1993) Algorithms, Routines and S Functions for Robust Statistics. Wadsworth & Brooks/Cole.

See Also

rlm, cov.trob

Examples

##############
# Skulls data

# make shorter labels for epochs and nicer variable labels in heplots
Skulls$epoch <- factor(Skulls$epoch, labels=sub("c","",levels(Skulls$epoch)))
# variable labels
vlab <- c("maxBreadth", "basibHeight", "basialLength", "nasalHeight")

# fit manova model, classically and robustly
sk.mod <- lm(cbind(mb, bh, bl, nh) ~ epoch, data=Skulls)
sk.rmod <- robmlm(cbind(mb, bh, bl, nh) ~ epoch, data=Skulls)

# standard mlm methods apply here
coefficients(sk.rmod)

# index plot of weights
plot(sk.rmod$weights, type="h", xlab="Case Index", ylab="Robust mlm weight", col="gray")
points(sk.rmod$weights, pch=16, col=Skulls$epoch)
axis(side=1, at=15+seq(0,120,30), labels=levels(Skulls$epoch), tick=FALSE, cex.axis=1)

# heplots to see effect of robmlm vs. mlm
heplot(sk.mod, hypotheses=list(Lin="epoch.L", Quad="epoch.Q"), 
    xlab=vlab[1], ylab=vlab[2], cex=1.25, lty=1)
heplot(sk.rmod, hypotheses=list(Lin="epoch.L", Quad="epoch.Q"), 
    add=TRUE, error.ellipse=TRUE, lwd=c(2,2), lty=c(2,2), 
    term.labels=FALSE, hyp.labels=FALSE, err.label="")

##############
# Pottery data

pottery.mod <- lm(cbind(Al,Fe,Mg,Ca,Na)~Site, data=Pottery)
pottery.rmod <- robmlm(cbind(Al,Fe,Mg,Ca,Na)~Site, data=Pottery)
Anova(pottery.mod)
Anova(pottery.rmod)

# index plot of weights
plot(pottery.rmod$weights, type="h")
points(pottery.rmod$weights, pch=16, col=Pottery$Site)

# heplots to see effect of robmlm vs. mlm
heplot(pottery.mod, cex=1.3, lty=1)
heplot(pottery.rmod, add=TRUE, error.ellipse=TRUE, lwd=c(2,2), lty=c(2,2), 
    term.labels=FALSE, err.label="")

###############
# Prestige data

# treat women and prestige as response variables for this example
prestige.mod <- lm(cbind(women, prestige) ~ income + education + type, data=Prestige)
prestige.rmod <- robmlm(cbind(women, prestige) ~ income + education + type, data=Prestige)

coef(prestige.mod)
coef(prestige.rmod)
# how much do coefficients change?
round(coef(prestige.mod) - coef(prestige.rmod),3)

# pretty plot of case weights
plot(prestige.rmod$weights, type="h", xlab="Case Index", ylab="Robust mlm weight", col="gray")
points(prestige.rmod$weights, pch=16, col=Prestige$type)
legend(0, 0.7, levels(Prestige$type), pch=16, col=palette()[1:3], bg="white")

heplot(prestige.mod, cex=1.4, lty=1)
heplot(prestige.rmod, add=TRUE, error.ellipse=TRUE, lwd=c(2,2), lty=c(2,2), 
    term.labels=FALSE, err.label="")

heplots documentation built on May 31, 2017, 4:54 a.m.