hoa (version 2.1.2)

rsm.surv: Fit a Regression-Scale Model Without Computing the Model Matrix

Description

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

Usage

rsm.surv(X, Y, offset, family, dispersion, score.dispersion, maxit, epsilon, 
         trace, ...)

Arguments

X

the model matrix (design matrix).

Y

the response vector.

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 extreme (Gumbel or extreme value), logWeibull, logExponential,

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.

score.dispersion

must default to NULL.

maxit

maximum number of iterations.

epsilon

convergence threshold.

trace

if TRUE, iterations details are printed during execution.

...

not used, but do absorb any redundant argument.

Value

• an object, which is a subset of a rsm object.

Details

The rsm.surv 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.

See Also

rsm, rsm.fit, rsm.null, rsm.object, rsm.families