| lm.summaries {stats} | R Documentation |
Accessing Linear Model Fits
Description
All these functions are methods for class "lm" objects.
Usage
## 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, ...)
Arguments
object, x |
an object inheriting from class |
... |
further arguments passed to or from other methods. |
type |
the type of residuals which should be returned. |
Details
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).
References
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.
See Also
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.
Examples
##-- 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))