lm.summaries              package: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"),
               ...)

     ## S3 method for class 'lm':
     labels(object, ...)

     weights(object, ...)

Arguments:

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.

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, 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.

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'.

     influence.measures for deletion diagnostics, including
     standardized ('rstandard') and studentized ('rstudent') residuals.

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))

