R: Summary Method for "lmrob" Objects

summary.lmrob {robustbase}

R Documentation

Summary Method for "lmrob" Objects

Description

Summary method for R object 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, ...)
## 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"), ...)

## S3 method for class 'lmrob'
vcov(object, cov = object$control$cov, ...)
## S3 method for class 'lmrob'
model.matrix(object, ...)

Arguments

`object`	an R object 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 R object 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.
`cov`	covariance estimation function to use.
`...`	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(<summary.obj>)` is slightly preferred to access this matrix.
`df`	degrees of freedom, in an `lm` compatible way.
`sigma`	identical to `sigma(object)`.
`aliased`	..

`cov.unscaled`	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.

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

[Package robustbase version 0.92-4 Index]