All these functions are
methods for class
## 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, ...))
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
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
"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
glm for generalized linear models,
influence (etc on that page) for regression diagnostics,
Documentation reproduced from R 3.0.2. License: GPL-2.