| 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
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
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))