Usage
timecox(formula=formula(data),data=sys.parent(),
start.time=0,max.time=NULL,id=NULL,clusters=NULL,n.sim=1000,
residuals=0,robust=1,Nit=20,bandwidth=0.5,method="basic",
weighted.test=0,degree=1,covariance=0)
Arguments
formula
a formula object with the response on the left of a '~' operator, and
the independent terms on the right as regressors. The response must be
a survival object as returned by the `Surv' function. Time-invariant
regressors are specified by the wrapper
data
a data.frame with the variables.
start.time
start of observation period where estimates are computed.
max.time
end of observation period where estimates are computed.
Estimates thus computed from [start.time, max.time]. Default is max of data.
robust
to compute robust variances and construct processes for
resampling. May be set to 0 to save memory.
id
For timevarying covariates the variable must
associate each record with the id of a subject.
clusters
cluster variable for computation of robust
variances.
n.sim
number of simulations in resampling.
weighted.test
to compute a variance weighted version of the
test-processes used for testing
time-varying effects.
residuals
to returns residuals that can be used for
model validation in the function cum.residuals
covariance
to compute covariance estimates for
nonparametric terms rather than just the variances.
Nit
number of iterations for score equations.
bandwidth
bandwidth for local iterations. Default is 50 %
of the range of the considered observation period.
method
Method for estimation. This refers to different parametrisations
of the baseline of the model. Options are "basic" where the baseline
is written as $\lambda_0(t) = \exp(\alpha_0(t))$
or the "breslow" version where the baseline is parametrised as
$\lambda
degree
gives the degree of the local linear smoothing, that is
local smoothing. Possible values are 1 or 2.