Generalized Linear Model Object
- DESCRIPTION:
-
Classes of objects returned by fitting generalized linear model objects.
- GENERATION:
-
This class of objects is returned by the glm function
to represent a fitted generalized linear model.
Class glm inherits from class lm, since it is fit by iterative reweighted
least squares; the object returned has all the components of a weighted least squares object.
The class of gam objects, on the other hand, inherit from class glm.
- METHODS:
-
Objects of this class have methods for the functions print,
plot, summary, anova, predict, fitted, drop1, add1, and step,
amongst others.
- STRUCTURE:
-
The following components must be included in a legitimate glm object.
The residuals, fitted values, coefficients and effects should be extracted
by the generic functions of the same name, rather than
by the "$" operator.
The family function returns the entire family object used in the fitting,
and deviance can be used to extract the deviance of the fit.
- coefficients:
-
the coefficients of the linear.predictors, which multiply the
columns of the model
matrix.
The names of the coefficients are the names of the
single-degree-of-freedom effects (the columns of the
model matrix).
If the model is over-determined there will
be missing values in the coefficients corresponding to inestimable
coefficients.
- linear.predictors:
-
the linear fit, given by the product of the model matrix and the coefficients;
also the fitted.values from the final weighted least squares fit.
- fitted.values:
-
the fitted mean values, obtained by transforming linear.predictors
using the inverse link function.
- residuals:
-
the residuals from the final weighted least squares fit; also known as
working
residuals, these are typically not interpretable without
rescaling by the weights.
- deviance:
-
up to a constant, minus twice the maximized log-likelihood.
Similar to the residual sum of squares.
- null.deviance:
-
the deviance corresponding to the model with no predictors.
- iter:
-
the number of IRLS iterations used to compute the estimates.
- family:
-
a 3 element character vector giving the name of the family,
the link and the variance function; mainly for printing purposes.
- weights:
-
the iterative weights from the final IRLS fit
The object will also have the components of an lm object:
coefficients, residuals, fitted.values, call, terms and some
others involving the numerical fit. See lm.object.
- SEE ALSO:
-
glm
,
gam.object
,
lm.object
.