coxaalen.control {coxinterval}  R Documentation 
Set parameters controlling the model fit returned by
coxaalen
.
coxaalen.control(eps = 1e07, eps.norm = c("max", "grad"), iter.max = 5000, armijo = 1/3, var.coef = TRUE, coef.typ = 1, coef.max = 10, trace = FALSE, thread.max = 1, data = FALSE)
eps 
threshold value for the norm used to measure convergence in the parameter estimates. 
eps.norm 
a character string identifying the norm to use in the convergence
criteria—either the maximum norm between the current and
previous parameter values ( 
iter.max 
maximum number of iterations to attempt. This ensures that

armijo 
a scale factor in (0, 1/2) for Armijo's (1966) rule—a line search used to ensure that each iteration achieves an adequate increase in the loglikelihood. The model fit is typically not very sensitive to this value. 
var.coef 
a logical value indicating that standard errors for the multiplicative regression coefficients should be estimated. This is done via profile likelihood—an approach that can require an inordinate amount of processing time under many regression coefficients and larger sample size. 
coef.typ 
a scalar or vector of typical (absolute) values for the multiplicative regression coefficient. 
coef.max 
a scalar or vector of probable upper bounds for the multiplicative
regression coefficient. This and the 
trace 
a logical value indicating that CPLEX should print its results to the screen. 
thread.max 
maximum number of CPU threads to allocate to CPLEX. The default value disables multithreading. A value of zero allows CPLEX to set the number of threads automatically. The actual number of threads used is limited by the number of available processors and the CPLEX license. 
data 
a logical value indicating that the object returned by

A list of the above arguments with their final values.
Boruvka, A. and Cook, R. J. (2015) A CoxAalen model for intervalcensored data. Scandinavian Journal of Statistics 42, 414–426.
Armijo, L. (1966) Minimization of functions having Lipschitz continuous first partial derivatives. Pacific Journal of Mathematics 16, 1–3.
if (is.loaded("coxaalen", "coxinterval")) coxaalen(Surv(left, right, type = "interval2") ~ prop(treat), data = cosmesis, control = coxaalen.control(iter.max = 2, trace = TRUE))