lm.summaries {base} | R Documentation |
All these functions are methods
for class "lm"
objects.
family(object, ...) formula(x, ...) residuals(object, type=c("working","response", "deviance","pearson", "partial"), ...) weights(object, ...)
object, x |
an object of class lm , usually, a result of a
call to lm . |
... |
further arguments passed to or from other methods. |
type |
the type of residuals which should be returned. |
The generic accessor functions coef
, effects
,
fitted
and residuals
can be used to extract
various useful features of the value returned by lm
.
The working and response residuals are ``observed - fitted''. The
deviance and pearson residuals are weighted residuals, scaled by the
square root of the weights used in fitting. The partial residuals
are a matrix with each column formed by omitting a term from the
model. In all these, zero weight cases are never omitted (as opposed
to the standardized rstudent
and similarobservations
The model fitting function lm
, anova.lm
.
coef
, deviance
,
df.residual
,
effects
, fitted
,
glm
for generalized linear models,
influence
(etc on that page) for regression diagnostics,
weighted.residuals
,
residuals
, residuals.glm
,
summary.lm
.
##-- Continuing the lm(.) example: coef(lm.D90)# the bare coefficients ## The 2 basic regression diagnostic plots [plot.lm(.) is preferred] plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's abline(h=0, lty=2, col = 'gray') qqnorm(residuals(lm.D90))