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()`, `confint()`, `dummy.coef()`, `hatvalues()`, etc., have explicitly defined `lmrob` methods. `.lmrob.hat()` is the lower level “work horse” of the `hatvalues()` method.

### 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"),
          showAlgo = TRUE, ...)

    ## S3 method for class 'lmrob'
    vcov(object, cov = object$control$cov, complete = TRUE, ...)
    ## 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.
`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; robustbase currently provides `".vcov.w"` and `".vcov.avar1"`, see Details of `lmrob`. Particularly useful when `object` is the result of `lmrob(.., cov = "none")`, where
      object$cov <- vcov(object, cov = ".vcov.w")
allows to update the fitted object.
`complete` | (mainly for R `>= 3.5.0`:) `logical` indicating if the full variance-covariance matrix should be returned also in case of an over-determined system where some coefficients are undefined and `coef(.)` contains `NA`s correspondingly. When `complete = TRUE`, `vcov()` is compatible with `coef()` also in this singular case.
`...` | 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` | 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

[Package robustbase version 0.95-0 Index]