| 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"),
...)
## S3 method for class 'lm'
labels(object, ...)
object, x |
an object inheriting from class |
... |
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, and the
weighted.residuals).
How residuals treats cases with missing values in the original
fit is determined by the na.action argument of that fit.
If na.action = na.omit omitted cases will not appear in the
residuals, whereas if na.action = na.exclude they will appear,
with residual value NA. See also naresid.
The "lm" method for generic labels returns the
term labels for estimable terms, that is the names of the terms with
an least one estimable coefficient.
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, weights.
influence.measures for deletion diagnostics, including
standardized (rstandard)
and studentized (rstudent) residuals.
##-- 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))