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.

the linear fit, given by the product of the model matrix and the coefficients.

the fitted mean values, obtained by transforming linear.predictors using the inverse link function.

the residuals from the final fit; also known as working residuals, they are typically not interpretable.

up to a constant, minus twice the log-likelihood evaluated at the final coefficients. Similar to the residual sum of squares.

the deviance corresponding to the model with no predictors.

a 3 element character vector giving the name of the family, the link and the variance function.

the number of linearly independent columns in the model matrix.

the number of degrees of freedom of the residuals.

a copy of the call that produced the object.

the same as the assign component of an "lm" object.

the same as the contrasts component of an "lm" object.

the same as the terms component of an "lm" object.

vector of the number of repetitions on the dependent variable. If the model is poisson then ni is a vector of 1s.

weights from the final fit.

number of iterations used to compute the estimates.

the dependent variable.

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.