Fit Robust Linear Regression Model

Usage

rlm(formula, data, weights, subset, na.action, 
     model=F, k=1.345, sw=1000, ...)

Arguments

formula a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.
data an optional data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument.
weights optional weights; if supplied, the algorithm fits to minimize the sum of the weights multiplied into the squared residuals. The weights must be strictly positive.
subset optional expression saying that only a subset of the rows of the data should be used in the fit.
na.action a missing-data filter function, applied to the model.frame, after any subset argument has been used.
model flag to control what is returned. If this is TRUE, then the model frame is returned. X and y are always returned.
k The control value for Winsorizing. The default gives 95% efficiency at the normal.
sw switch to Huber proposal 2 scale at iteration sw and beyond.
... additional arguments for the fitting routines. The most likely one is maxit, which sets the iteration limit, by default 20.

Description

Fits a robust linear regression model, using an M-estimator with Huber's psi function.

Details

The fit uses Huber's M-estimator, and initially uses the median absolute deviation scale estimate based on the residuals. This can be changed to Huber's proposal 2 after sw iterations.

Generic functions such as print and summary have methods to show the results of the fit.

Value

an object of class rlm representing the fit, inheriting from lm. This has all the components of an lm object, plus k, the scale s and conv which is a vector monitoring the convergence.

See Also

summary.rlm

Examples

data(phones)
attach(phones)
res <- rlm(calls ~ year)
print(res)

data(stack)
rlm(stack.loss ~ stack.x)

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