smoothSurvReg class of functions to represent
a fitted smoothed survival regression model.Objects of this class have methods for the functions print, summary, plot,
residuals, survfit.
smoothSurvReg object.
fail component is increased by 10 if the final minus Hessian of the penalized
log-likelihood was not positive definite.
The fail component is further increased by 20 if the computed effective
degrees of freedom were non-positive.
The fail component is further increased by 40 if there are negative estimates
of standard errors for some regression parameters.
The fail component is 99 or higher if the fitting procedure failed at all and
there is no fit produced.
smoothSurvReg object
if fail is lower than 99.
colnames ``Value'', ``Std.Error'', ``Std.Error2'' and rownames derived from
the names of the design matrix with ``(Intercept)'' for the intercept, ``Scale'' for the scale
and ``Log(scale)'' for the log-scale. If the log-scale depends on covariates then rows
named ``LScale.(Intercept)'', ``LScale.cov1'' etc. give estimates of regression parameters
for log-scale.
The two standard errors are computed using either
var or var2 described below.
colnames ``Knot'', ``SD basis'',
``c coef.'', ``Std.Error.c'', ``Std.Error2.c'',
``a coef.'', ``Std.Error.a'' and ``Std.Error2.a''
and rownames knot[1], ..., knot[g] where
$g$ stands for the number of basis G-splines. The column ``Knot'' contains the knots
in ascending order, ``SD basis'' the standard deviation of an appropriate basis G-spline,
``c coef.'' estimates of the G-spline coefficients and ``Std.Error.c'' and ``Std.Error2.c''
the estimates of their standard errors based either on var or var2.
The column ``a coef.'' contains the estimates of transformed $c$ coefficients where
$$c_j = \frac{\exp(a_j)}{\sum_{l=1}^{g}\exp(a_l)}, j = 1,\dots, g.$$
If the error distribution is estimated, one of the $a$ coefficients is set to zero and
two other $a$'s are expressed as a function of the remaining $a$ coefficients
(to avoid equality constraints concerning the mean and the variance of the error distribution).
The standard error for these three $a$ coefficients is then not available (it is equal
to NA).
Standard error is set to
NaN is a diagonal element of the appropriate covariance matrix was negative.
fail component
of the object. It contains a string ``OK'' if there are no problems with the appropriate part
of the fitting process.
na.action attribute, if any, that was returned by the na.action routine.
terms object used.
regres component of the smoothSurvReg object.
regres component and
``Scale'' is equal to the ``Scale'' of either regres or init.regres component.
NA's appeare in this data.frame in the case that log-scale depends on covariates.