rsm.surv {marg} | R Documentation |
Fits a rsm
model without computing the model matrix of the
response vector.
rsm.surv(X, Y, offset, family, dispersion, maxit, epsilon, trace, ...)
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 , logRayleigh ,
logistic and student (Student's t) with df
larger than 2.
|
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.
|
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. |
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.
an object, which is a subset of a rsm
object.
The rsm.surv
function is the default option for
rsm
for the extreme
, logistic
,
logWeibull
, logExponential
, logRayleigh
and
student
(with df
larger than 2) error distributions.
It makes use of the survreg.fit
routine to
estimate parametric survival models. It receives X
and
Y
data rather than a formula, but still uses the
family.rsm
object to define the IRLS steps. The
rsm.surv
routine cannot be used for Huber-type and
user-defined error distributions.
rsm
, rsm.fit
, rsm.null
,
rsm.object
, rsm.families