lm.summaries {base}

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 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.

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 and similarobservations

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))

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