Summary Method for Robust Linear Models
summary method for objects of class
## S3 method for class 'rlm': summary(object, method = c("XtX", "XtWX"), correlation = FALSE, ...)
- 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
rlmfunction will be available.
- Should the weighted (by the IWLS weights) or unweighted cross-products matrix be used?
- logical. Should correlations be computed (and printed)?
- arguments passed to or from other methods.
This function is a method for the generic function
summary() for class
It can be invoked by calling
summary(x) for an
x of the appropriate class, or directly by
summary.rlm(x) regardless of the
class of the object.
- 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
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.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
summary(rlm(calls ~ year, data = phones, maxit = 50)) Call: rlm(formula = calls ~ year, data = phones, maxit = 50) Residuals: Min 1Q Median 3Q Max -18.31 -5.95 -1.68 26.46 173.77 Coefficients: Value Std. Error t value (Intercept) -102.622 26.553 -3.86 year 2.041 0.429 4.76 Residual standard error: 9.03 on 22 degrees of freedom Correlation of Coefficients:  -0.994