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)