emfrail_control {frailtyEM}

Control parameters for emfrail

Description

Control parameters for emfrail

Usage

emfrail_control(opt_fit = TRUE, se = TRUE, se_adj = TRUE,
  ca_test = TRUE, lik_ci = TRUE, lik_interval = exp(c(-3, 20)),
  lik_interval_stable = exp(c(0, 20)), nlm_control = list(stepmax = 1),
  zph = FALSE, zph_transform = "km", em_control = list(eps = 1e-04,
  maxit = Inf, fast_fit = TRUE, verbose = FALSE, upper_tol = exp(10),
  lik_tol = 1))

Arguments

opt_fit

Logical. Whether the outer optimization should be carried out. If FALSE, then the frailty parameter is treated as fixed and the emfrail function returns only log-likelihood. See details.

se

Logical. Whether to calculate the variance / covariance matrix.

se_adj

Logical. Whether to calculate the adjusted variance / covariance matrix (needs se == TRUE)

ca_test

Logical. Should the Commenges-Andersen test be calculated?

lik_ci

Logical. Should likelihood-based confidence interval be calculated for the frailty parameter?

lik_interval

The edges, on the scale of \theta, of the parameter space in which to search for likelihood-based confidence interval

lik_interval_stable

(for dist = "stable") The edges, on the scale of \theta, of the parameter space in which to search for likelihood-based confidence interval

nlm_control

A list of named arguments to be sent to nlm for the outer optimization.

zph

Logical. Should the cox.zph test be performed at the maximum likelihood estimate?

zph_transform

One of "km", "rank", "identity" or a function of one argument to be pased on to cox.zph.

em_control

A list of parameters for the inner optimization. See details.

Details

The nlm_control argument should not overalp with hessian, f or p.

The em_control argument should be a list with the following items:

• eps A criterion for convergence of the EM algorithm (difference between two consecutive values of the log-likelihood)

• maxit The maximum number of iterations between the E step and the M step

• fast_fit Logical, whether the closed form formulas should be used for the E step when available

• verbose Logical, whether details of the optimization should be printed

• upper_tol An upper bound for \theta; after this treshold, the algorithm returns the limiting log-likelihood of the no-frailty model. That is because the no-frailty scenario corresponds to a \theta = \infty, which could lead to some numerical issues

• lik_tol For values higher than this, the algorithm returns a warning when the log-likelihood decreases between EM steps. Technically, this should not happen, but if the parameter \theta is somewhere really far from the maximum, numerical problems might lead in very small likelihood decreases.

The fast_fit option make a difference when the distribution is gamma (with or without left truncation) or inverse Gaussian, i.e. pvf with m = -1/2 (without left truncation). For all the other scenarios, the fast_fit option will automatically be changed to FALSE. When the number of events in a cluster / individual is not very small, the cases for which fast fitting is available will show an improvement in performance.

The starting value of the outer optimization may be set in the distribution argument.

Value

An object of the type emfrail_control.

See Also

emfrail, emfrail_dist, emfrail_pll

Examples

emfrail_control()
emfrail_control(em_control = list(eps = 1e-7))