glm.summaries {stats} | R Documentation |
Accessing Generalized Linear Model Fits
Description
These functions are all methods
for class glm
or
summary.glm
objects.
Usage
## S3 method for class 'glm'
family(object, ...)
## S3 method for class 'glm'
residuals(object, type = c("deviance", "pearson", "working",
"response", "partial"), ...)
Arguments
object |
an object of class |
type |
the type of residuals which should be returned.
The alternatives are: |
... |
further arguments passed to or from other methods. |
Details
The references define the types of residuals: Davison and Snell (1991) 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.
References
Davison A, Snell EJ (1991). “Residuals and Diagnostics.” In Hinkley DV, Reid N, Snell EJ (eds.), Statistical Theory and Modelling. In Honour of Sir David Cox, FRS. Chapman & Hall.
McCullagh P, Nelder JA (1989). Generalized Linear Models. Chapman and Hall/CRC, London. ISBN 978-0412317606.
Hastie TJ, Pregibon D (1992). “Generalized Linear Models.” In Chambers JM, Hastie TJ (eds.), Statistical Models in S, chapter 6. Wadsworth & Brooks/Cole.
See Also
glm
for computing glm.obj
, anova.glm
;
the corresponding generic functions, summary.glm
,
coef
, deviance
,
df.residual
,
effects
, fitted
,
residuals
.
influence.measures for deletion diagnostics, including
standardized (rstandard
)
and studentized (rstudent
) residuals.