| lm.summaries {stats} | R Documentation |
All these functions are methods for class "lm" objects.
## S3 method for class 'lm':
family(object, ...)
## S3 method for class 'lm':
formula(x, ...)
## S3 method for class 'lm':
residuals(object,
type = c("working", "response", "deviance","pearson", "partial"),
...)
weights(object, ...)
object, x |
an object inheriting from class lm, usually
the result of a call to lm or aov. |
... |
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 residuals).
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))