R: Regression-Scale Model Object

rsm.object {marg}

R Documentation

Regression-Scale Model Object

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