Description Usage Arguments Details Value Author(s) References See Also Examples
Fit a survival model using either a semi-parametric approach (penalized likelihood with an approximation of the hazard function by linear combination of M-splines) or a parametric approach (specifying a Weibull distribution on the hazard function). Left-truncated, right-censored, and interval-censored data are allowed.
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formula |
a formula object with the response on the left of a \texttildelow operator, and the terms on the right. The response must be a survival object or Hist object as returned by the 'Surv' or 'Hist' function. |
data |
a data frame in which to interpret the variables named
in the |
eps |
a vector of length 3 for the convergence criteria (criterion for parameters, criterion for likelihood, criterion for second derivatives). The default is 'c(5,5,3)' and corresponds to criteria equals to 10^{-5}, 10^{-5} and 10^{-3}. |
nknots |
number of knots for the splines to use to approximate the hazard function. Argument for the penalized likelihood approach. The default is 7. |
CV |
binary variable equals to 1 when search (by approximated cross validation) of the smoothing parameter kappa and 0 otherwise. Argument for the penalized likelihood approach. The default is 0. |
kappa |
if CV=FALSE, smoothing parameter; if CV=TRUE, initial value of the smoothing parameters for the cross validation search. Argument for the penalized likelihood approach. |
conf.int |
Boolean parameter. Equals to |
maxiter |
maximum number of iterations. The default is 200. |
method |
type of estimation method: "Splines" for a penalized likelihood approach with approximation of the hazard function by M-splines, "Weib" for a parametric approach with a Weibull distribution on the hazard function. Default is "Weib". |
print.iter |
boolean parameter. Equals to |
na.action |
how NAs are treated. The default is first, any na.action attribute of data, second a na.action setting of options, and third 'na.fail' if that is unset. The 'factory-fresh' default is na.omit. Another possible value is NULL. |
The estimated parameters are obtained using the robust Marquardt algorithm (Marquardt, 1963) which is a combination between a Newton-Raphson algorithm and a steepest descent algorithm.
call |
|
coef |
regression parameters. |
loglik |
vector containing the log-likelihood without and with covariate. |
modelPar |
Weibull parameters. |
N |
number of subjects. |
NC |
number of covariates. |
nevents |
number of events. |
modelResponse |
model response: |
converged |
integer equal to 1 when the model converged, 2, 3 or 4 otherwise. |
time |
times for which survival and hazard functions have been evaluated for plotting. |
hazard |
matched values of the hazard function. |
lowerHazard |
lower confidence limits for hazard function. |
upperHazard |
upper confidence limits for hazard function. |
surv |
matched values of the survival function. |
lowerSurv |
lower confidence limits for survival function. |
upperSurv |
upper confidence limits for survival function. |
RR |
vector of relative risks. |
V |
variance-covariance matrix. |
se |
standart errors. |
knots |
knots to approximate by M-splines the hazard function. |
nknots |
number of knots. |
CV |
a binary variable equals to 1 when search of the smoothing parameter kappa by approximated cross-validation, 1 otherwise. The default is 0. |
niter |
number of iterations. |
cv |
vector containing the convergence criteria. |
na.action |
observations deleted if missing values. |
R: Celia Touraine <Celia.Touraine@isped.u-bordeaux2.fr> Fortran: Pierre Joly <Pierre.Joly@isped.u-bordeaux2.fr>
D. Marquardt (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 431-441.
shr
, print.shr
,
summary.shr
, print.shr
,
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