robustbase (version 0.92-5)

summary.lmrob: Summary Method for "lmrob" Objects

Description

Summary method for Robject of class "lmrob" and print method for the summary object.

Further, methods fitted(), residuals() work (via the default methods), and predict() (see predict.lmrob, vcov(), weights() (see weights.lmrob), model.matrix(), etc. have explicitly defined lmrob methods.

Usage

## S3 method for class 'lmrob':
summary(object, correlation = FALSE,
        symbolic.cor = FALSE, \dots)
## S3 method for class 'summary.lmrob':
print(x, digits = max(3, getOption("digits") - 3),
      symbolic.cor= x$symbolic.cor,
      signif.stars = getOption("show.signif.stars"),
      showAlgo = TRUE, ...)
## S3 method for class 'lmrob':
vcov(object, cov = object$control$cov, \dots)
## S3 method for class 'lmrob':
model.matrix(object, \dots)

Arguments

object

an Robject of class lmrob, typically created by lmrob.

correlation

logical variable indicating whether to compute the correlation matrix of the estimated coefficients.

symbolic.cor

logical indicating whether to use symbols to display the above correlation matrix.

x

an Robject of class summary.lmrob, typically resulting from summary(lmrob(..),..).

digits

number of digits for printing, see digits in options.

signif.stars

logical variable indicating whether to use stars to display different levels of significance in the individual t-tests.

showAlgo

optional logical indicating if the algorithmic parameters (as mostly inside the control part) should be shown.

cov

covariance estimation function to use, a function or character string naming the function.

...

potentially more arguments passed to methods.

Value

• summary(object) returns an object of S3 class "summary.lmrob", basically a list with components "call", "terms", "residuals", "scale", "rweights", "converged", "iter", "control" all copied from object, and further components, partly for compatibility with summary.lm,
• coefficients
  a matrix with columns "Estimate", "Std. Error", "t value", and "PR(>|t|)", where "Estimate" is identical to coef(object). Note that coef() is slightly preferred to access this matrix.
• df
  degrees of freedom, in an lm compatible way.
• sigma
  identical to sigma(object).
• aliased
  ..
• cov
  derived from object$cov.
• r.squared
  robust R squared or $R^2$, a coefficient of determination: This is the consistency corrected robust coefficient of determination by Renaud and Victoria-Feser (2010).
• adj.r.squared
  an adjusted R squared, see r.squared.

References

Renaud, O. and Victoria-Feser, M.-P. (2010). A robust coefficient of determination for regression, Journal of Statistical Planning and Inference 140, 1852-1862.

See Also

lmrob, predict.lmrob, weights.lmrob, summary.lm, print, summary.

Examples

mod1 <- lmrob(stack.loss ~ ., data = stackloss)
sa <- summary(mod1)  # calls summary.lmrob(....)
sa                   # dispatches to call print.summary.lmrob(....)

## correlation between estimated coefficients:
cov2cor(vcov(mod1))

cbind(fit = fitted(mod1), resid = residuals(mod1),
      wgts= weights(mod1, type="robustness"),
      predict(mod1, interval="prediction"))

data(heart)
sm2 <- summary( m2 <- lmrob(clength ~ ., data = heart) )
sm2