summary.lmrob {robustbase} | R Documentation |
Summary method for R object of class "lmrob"
and
print
method for the summary object.
Further, methods fitted()
, residuals()
or
weights()
work (via the default methods), and
predict()
(see predict.lmrob
,
vcov()
, model.matrix()
have explicitly
defined lmrob
methods.
## 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, ...)
object |
an R object of class |
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 |
digits |
number of digits for printing, see |
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. |
lmrob
, predict.lmrob
,
summary.lm
,
print
, summary
.
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), predict(mod1, interval="prediction")) data(heart) sm2 <- summary( m2 <- lmrob(clength ~ ., data = heart) ) sm2