summary.rlm: Summary Method for Robust Linear Models

In MASS: Support Functions and Datasets for Venables and Ripley's MASS

Summary Method for Robust Linear Models

Description

summary method for objects of class "rlm"

Usage

## S3 method for class 'rlm'
summary(object, method = c("XtX", "XtWX"), correlation = FALSE, ...)

Arguments

object	the fitted model. This is assumed to be the result of some fit that produces an object inheriting from the class rlm, in the sense that the components returned by the rlm function will be available.
method	Should the weighted (by the IWLS weights) or unweighted cross-products matrix be used?
correlation	logical. Should correlations be computed (and printed)?
...	arguments passed to or from other methods.

Details

This function is a method for the generic function summary() for class "rlm". It can be invoked by calling summary(x) for an object x of the appropriate class, or directly by calling summary.rlm(x) regardless of the class of the object.

Value

If printing takes place, only a null value is returned. Otherwise, a list is returned with the following components. Printing always takes place if this function is invoked automatically as a method for the summary function.

correlation	The computed correlation coefficient matrix for the coefficients in the model.
cov.unscaled	The unscaled covariance matrix; i.e, a matrix such that multiplying it by an estimate of the error variance produces an estimated covariance matrix for the coefficients.
sigma	The scale estimate.
stddev	A scale estimate used for the standard errors.
df	The number of degrees of freedom for the model and for residuals.
coefficients	A matrix with three columns, containing the coefficients, their standard errors and the corresponding t statistic.
terms	The terms object used in fitting this model.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

summary

Examples

summary(rlm(calls ~ year, data = phones, maxit = 50))

MASS documentation built on June 22, 2024, 10:42 a.m.