rsm.fit: Fit a Regression-Scale Model Without Computing the Model...
In hoa: Higher Order Likelihood Inference

Description Usage Arguments Details Value Note See Also

Fits a rsm model without computing the model matrix of the response vector.

1 2	rsm.fit(X, Y, offset, family, dispersion, score.dispersion, maxit, epsilon, trace, ...)

`X`	the model matrix (design matrix).
`Y`	the response vector.
`dispersion`	if `NULL`, the MLE of the scale parameter is returned, otherwise the scale parameter is fixed to the numerical value passed through the argument. If Huber's least favourable distribution is used and `dispersion` is `TRUE`, the MAD is computed and the scale parameter fixed to this value in subsequent calculations.
`score.dispersion`	must default to `NULL`.
`offset`	optional offset added to the linear predictor.
`family`	a `family.rsm` object, i.e. a list of functions and expressions characterizing the error distribution. Families supported are `gaussian`, `student` (Student's t), `extreme` (Gumbel or extreme value), `logistic`, `logWeibull`, `logExponential`, `logRayleigh` and `Huber` (Huber's least favourable). Users can construct their own families, as long as they have components compatible with those given in `rsm.distributions`. The demonstration file ‘margdemo.R’ that ships with the package shows how to create a new generator function.
`maxit`	maximum number of iterations allowed.
`epsilon`	convergence threshold.
`trace`	if `TRUE`, iterations details are printed during execution.
`...`	not used, but do absorb any redundant argument.

The rsm.fit function is called internally by the rsm routine to do the actual model fitting. Although it is not intended to be used directly by the user, it may be useful when the same data frame is used over and over again. It might save computational time, since the model matrix is not created. No formula needs to be specified as an argument. As no weights argument is available, the response Y and the model matrix X must already include the weights if weighting is desired.

an object which is a subset of a rsm object.

The rsm.fit function is the workhorse of the rsm fitting routine for the student (with df less or equal 2), Huber and user-defined error distributions. It receives X and Y data rather than a formula, but still uses the family.rsm object to define the IRLS steps. Users can write their own versions of rsm.fit, and pass the name of their function via the method argument to rsm. Care should be taken to include as many of the arguments as feasible, but definitely the ... argument, which will absorb any additional argument given in the call from rsm.