| intcox {intcox} | R Documentation |
Intcox fits the Cox proportional hazards model for interval censored data by the Iterative Convex Minorant Algorithm (ICM)
intcox(formula = formula(data), data = parent.frame(), subset, na.action, x = FALSE, y = TRUE, epsilon = 1e-04, itermax = 10000, no.warnings = FALSE)
formula |
a formula object, with the response on the left of a ~ operator, and the terms on the right.
The response must be a survival object of type "interval2" as returned by the Surv function. |
data |
a data.frame in which to interpret the variables named in the formula, or in the subset argument. |
subset |
expression saying that only a subset of the rows of the data should be used in the fit. |
na.action |
a missing-data filter function, applied to the model.frame, after any subset argument has been used. Default is options()$na.action. |
x |
Return the design matrix in the model object? |
y |
Return the response in the model object? |
epsilon |
convergence treshold. Iteration will continue until the relative change in the log-likelihood is less then epsilon. Default is .0001. |
itermax |
maximum number of iteration |
no.warnings |
logical value indicating how to handle warnings. If TRUE, warnings will be displayed. Default is FALSE. |
With this package the Cox proportional hazards model can be applied for interval censored data. It tries to maximise the log-likelihood by a simultanious improvement of the coefficients and the cumulative hazard function in the gradient direction weighted by the main diagonal elements of the negative Hessian matrix.
an object of class "coxph". See coxph.object for details. Not all features are realised.
Not realized features result in NA, e.g. se, z and p.
Additionally there are given
lambda0 |
estimated baseline hazard |
time.point |
corresponding time points for the steps |
likeli.vec |
vector of the estimated loglik of each step |
termination |
indicator for the reason of termination, 1 - algorithm converged 2 - no improvement of likelihood possible, the iteration number is shown 3 - algorithm did not converge - maximum number of iteration reached 4 - inside precondition(s) are not fulfilled at this iteration |
Ch. Heiss, V. Henschel, U. Mansmann
Wei Pan, (1999), Extending the Iterative Convex Minorant Algorithm to the Cox Model for Interval-Censored Data, Journal of Computational & Graphical Statistics, vol. 8, pp. 109-120
data(intcox.example) intcox(Surv(left,right,type="interval2")~x.1+x.2+x.3+x.4,data=intcox.example)