Robust Generalized Linear Model Objects

DESCRIPTION:

These are objects of class glmRob which represent the robust fit of a generalized linear regression model, as estimated by glmRob().

GENERATION:

This class of objects is returned from the glmRob function.

METHODS:

anova , coefficients , deviance , fitted.values , family , formula, plot , print, residuals , summary .

STRUCTURE:

The following components must be included in a legitimate "glmRob" object. Residuals, fitted values, and coefficients 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.

VALUE:

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.
fitted.values
the fitted mean values, obtained by transforming linear.predictors using the inverse link function.
residuals
the residuals from the final fit; also known as working residuals, they are typically not interpretable.
deviance
up to a constant, minus twice the log-likelihood evaluated at the final coefficients . Similar to the residual sum of squares.
null.deviance
the deviance corresponding to the model with no predictors.
family
a 3 element character vector giving the name of the family, the link and the variance function.
rank
the number of linearly independent columns in the model matrix.
df.residuals
the number of degrees of freedom of the residuals.
call
a copy of the call that produced the object.
assign
the same as the assign component of an "lm" object.
contrasts
the same as the contrasts component of an "lm" object.
terms
the same as the terms component of an "lm" object.
ni
vector of the number of repetitions on the dependent variable. If the model is poisson then ni is a vector of 1 s.
weights
weights from the final fit.
iter
number of iterations used to compute the estimates.
y
the dependent variable.
contrasts
the same as the contrasts term of an "lm" object. The object will also contain other components related to the numerical fit that are not relevant for the associated methods.

SEE ALSO:

.