rsm.object: Regression-Scale Model Object

In marg: Approximate Marginal Inference for Regression-Scale Models

Description

Class of objects returned when fitting a regression-scale model.

Arguments

The following components must be included in a rsm object:

coefficients	the coefficients of the linear predictor, 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.
dispersion	the (estimated or known) value of the scale parameter.
fixed	a logical value. If TRUE, the scale parameter is fixed.
residuals	the response residuals from the fit. If weights were used, they are not taken into account. If you need other kinds of residuals, use the residuals.rsm function.
fitted.values	the fitted values from the fit. If weights were used, the fitted values are not adjusted for the weights.
loglik	the log likelihood from the fit.
q1	the value of the first derivative of minus the log density for each observation.
q2	the value of the second derivative of minus the log density for each observation.
rank	the computed rank (number of linearly independent columns in the model matrix).
R	the unscaled observed information matrix.
score.dispersion	a list containing the value of the objective function, its gradient and the convergence diagnostic, that result from estimating the scale parameter.
iter	the number of IRLS iterations used to compute the estimates.
weights	the (optional) weights used for the fit.
assign	the list of assignments of coefficients (and effects) to the terms in the model. The names of this list are the names of the terms. The ith element of the list is the vector saying which coefficients correspond to the ith term. It may be of length 0 if there were no estimable effects for the term.
df.residuals	the number of degrees of freedom for residuals.
family	the entire family.rsm object used.
user.def	a logical value. If TRUE, the error distribution is user-defined.
dist	a character string representing the name of the error distribution.
formula	the model formula.
data	the data frame in which to interpret the variables occurring in the model formula, or in the subset and the weights arguments to rsm.
terms	an object of mode expression and class term summarizing the formula.
contrasts	a list containing sufficient information to construct the contrasts used to fit any factors occurring in the model. The list contains entries that are either matrices or character vectors. When a factor is coded by contrasts, the corresponding contrast matrix is stored in this list. Factors that appear only as dummy variables and variables in the model that are matrices correspond to character vectors in the list. The character vector has the level names for a factor or the column labels for a matrix.
control	a list of iteration and algorithmic constants used in rsm to fit the model.
call	an image of the call that produced the object, but with the arguments all named and with the actual formula included as the formula argument.
y	optionally the response, if y = TRUE in the original rsm call.
x	optionally the model matrix, if x = TRUE in the original rsm call.
model	optionally the model frame, if model = TRUE in the original rsm call.

Generation

This class of objects is returned by the rsm function to represent a fitted regression-scale model. Class rsm inherits from classes glm and lm, since it is fitted by iteratively reweighted least squares. The object returned has all the components of a weighted least squares object.

Methods

Objects of this class have methods for the functions print, summary, anova and fitted among others.

Note

The residuals, fitted values and coefficients should be extracted by the generic functions of the same name, rather than by the $ operator.

See Also

rsm, glm, lm.

marg documentation built on May 2, 2019, 7:55 a.m.