| glm.summaries {stats} | R Documentation |
These functions are all methods for class glm or
summary.glm objects.
## S3 method for class 'glm':
family(object, ...)
## S3 method for class 'glm':
residuals(object, type = c("deviance", "pearson", "working",
"response", "partial"), ...)
object |
an object of class glm, typically the result of
a call to glm. |
type |
the type of residuals which should be returned.
The alternatives are: "deviance" (default), "pearson",
"working", "response", and "partial". |
... |
further arguments passed to or from other methods. |
The references define the types of residuals: Davison & Snell is a good reference for the usages of each.
The partial residuals are a matrix of working residuals, with each column formed by omitting a term from the model.
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.
For fits done with y = FALSE the response values are computed
from other components.
Davison, A. C. and Snell, E. J. (1991) Residuals and diagnostics. In: Statistical Theory and Modelling. In Honour of Sir David Cox, FRS, eds. Hinkley, D. V., Reid, N. and Snell, E. J., Chapman & Hall.
Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
glm for computing glm.obj, anova.glm;
the corresponding generic functions, summary.glm,
coef, deviance,
df.residual,
effects, fitted,
residuals.